Search results โ€œWeb mining with relational clustering mathโ€
Data Mining  Association Rule - Basic Concepts
short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
Mining Multilevel Association Rules ll DMW ll Concept Hierarchy ll Explained with Examples in Hindi
๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 11574 5 Minutes Engineering
Moving and Clustering Data with Sqoop and Spark
Efficiently transferring bulk data is an essential Big Data skill. Learn how to cluster dataโˆ’key technique for statistical data analysisโˆ’using Apache Sqoopโ„ข and Apache Sparkโ„ข tp evaluate flu data.
Views: 1348 OracleAcademyChannel
Views: 38221 Data Mining - IITKGP
Applications of clustering in IR Initialization of K means
Education, Medicine, Health, Healthy lifestyle, Physics, Chemistry, Maths, Nihilist
Views: 48 Nihilist
Mining Your Logs - Gaining Insight Through Visualization
Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25444 GoogleTechTalks
Applying a Transformation
In this demonstration, you learn how to apply a transformation to your model using Oracle SQL Developer Data Modeler Release 3.1. Copyright ยฉ 2012 Oracle and/or its affiliates. Oracleยฎ is a registered trademark of Oracle and/or its affiliates. All rights reserved. Oracle disclaims any warranties or representations as to the accuracy or completeness of this recording, demonstration, and/or written materials (the "Materials"). The Materials are provided "as is" without any warranty of any kind, either express or implied, including without limitation warranties of merchantability, fitness for a particular purpose, and non-infringement.
Final Year Projects | An Enhanced Fuzzy Similarity Based Concept Mining Model
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 65 myproject bazaar
Data Academy Relational Transformations
Demonstration of the Award Winning SQL Server Data Warehouse Tool Data Academy
Views: 100 DataAcademy
SQLite: How to find patterns in data | lynda.com tutorial
This SQLite tutorial shows how to find patterns in data using the LIKE operator. Watch more at http://www.lynda.com/SQLite-3-with-PHP-tutorials/essential-training/66386-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-66386-0603 This specific tutorial is just a single movie from chapter six of the SQLite 3 with PHP Essential Training course presented by lynda.com author Bill Weinman. The complete SQLite 3 with PHP Essential Training course has a total duration of 6 hours and covers the fundamentals of SQLite, including a thorough overview of its unique data type system, expressions, functions, transactions, views, and event triggers SQLite 3 with PHP Essential Training table of contents: Introduction 1. Quick Start 2. Getting Started 3. Creating a Database 4. SQLite Data Types 5. Storing and Reading Data 6. SQLite Expressions 7. SQLite Core Functions 8. SQLite Aggregate Functions 9. SQLite Date and Time Functions 10. Sorting and Indexing 11. Transactions 12. Subselects and Views 13. Triggers 14. PHP Interfaces 15. A Simple CRUD Application 16. An Application for Web Site Testimonials Conclusion
Views: 1522 LinkedIn Learning
Data Mining Lecture 16 Part 2
Regression : Column Sampling and Frequent Directions
Views: 175 Utah Data
What is Geometric primitive?, Explain Geometric primitive, Define Geometric primitive
#Geometricprimitive #audioversity ~~~ Geometric primitive ~~~ Title: What is Geometric primitive?, Explain Geometric primitive, Define Geometric primitive Created on: 2019-01-15 Source Link: https://en.wikipedia.org/wiki/Geometric_primitive ------ Description: The term geometric primitive, or prim, in computer graphics and CAD systems is used in various senses, with the common meaning of the simplest geometric objects that the system can handle . Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segment, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are lines , as well as shapes . A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles. All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. ------ To see your favorite topic here, fill out this request form: https://docs.google.com/forms/d/e/1FAIpQLScU0dLbeWsc01IC0AaO8sgaSgxMFtvBL31c_pjnwEZUiq99Fw/viewform ------ Source: Wikipedia.org articles, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Support: Donations can be made from https://wikimediafoundation.org/wiki/Ways_to_Give to support Wikimedia Foundation and knowledge sharing.
Views: 10 Audioversity
Lecture - 34 Data Mining and Knowledge Discovery
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 134562 nptelhrd
Using the Pandas Library for Analyzing Time Series Data: From Data Just Right LiveLessons
http://www.informit.com/store/data-just-right-livelessons-video-training-9780133807141 Using the Pandas Library for Analyzing Time Series Data is a video sample excerpt from, Data Just Right LiveLessons Video Training -- 7 Hours of Video Instruction Overview Data Just Right LiveLessons provides a practical introduction to solving common data challenges, such as managing massive datasets, visualizing data, building data pipelines and dashboards, and choosing tools for statistical analysis. You will learn how to use many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Data Just Right LiveLessons shows how to address each of today's key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You'll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. These videos demonstrate techniques using many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery. Data Engineer and former Googler Michael Manoochehri provides viewers with an introduction to implementing practical solutions for common data problems. The course does not assume any previous experience in large scale data analytics technology, and includes detailed, practical examples. Skill Level Beginner What You Will Learn Mastering the four guiding principles of Big Data success--and avoiding common pitfalls Emphasizing collaboration and avoiding problems with siloed data Hosting and sharing multi-terabyte datasets efficiently and economically "Building for infinity" to support rapid growth Developing a NoSQL Web app with Redis to collect crowd-sourced data Running distributed queries over massive datasets with Hadoop and Hive Building a data dashboard with Google BigQuery Exploring large datasets with advanced visualization Implementing efficient pipelines for transforming immense amounts of data Automating complex processing with Apache Pig and the Cascading Java library Applying machine learning to classify, recommend, and predict incoming information Using R to perform statistical analysis on massive datasets Building highly efficient analytics workflows with Python and Pandas Establishing sensible purchasing strategies: when to build, buy, or outsource Previewing emerging trends and convergences in scalable data technologies and the evolving role of the "Data Scientist" Who Should Take This Course Professionals who need practical solutions to common data challenges that they can implement with limited resources and time. Course Requirements Basic familiarity with SQL Some experience with a high-level programming language such as Java, JavaScript, Python, R Experience working in a command line environment http://www.informit.com/store/data-just-right-livelessons-video-training-9780133807141
Views: 3877 LiveLessons
First Normal Form ll 1NF Explained with Solved Example in Hindi ll Normalization ll DBMS
๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Overview of ๏ฟฝBig Data๏ฟฝ Research at TU Berlin
Intro - By Volker Markl Part 1 - Query Optimization with MapReduce Functions, Kostas Tzoumas Abstract: Many systems for big data analytics employ a data flow programming abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators using a shallow static code analysis pass based on reverse data and control flow analysis over the general-purpose code of their user-defined functions. We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of non-relational data flows, a unique feature among today's systems. Part 2 - Spinning Fast Iterative Data Flows, Stephan Ewen Abstract: Parallel data flow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk iterative algorithms are supported by novel data flow frameworks, these systems cannot exploit computational dependencies present in many algorithms, such as graph algorithms. As a result, these algorithms are inefficiently executed and have led to specialized systems based on other paradigms, such as message passing or shared memory. We propose a method to integrate "incremental iterations", a form of workset iterations, with parallel data flows. After showing how to integrate bulk iterations into a dataflow system and its optimizer, we present an extension to the programming model for incremental iterations. The extension alleviates for the lack of mutable state in dataflows and allows for exploiting the "sparse computational dependencies" inherent in many iterative algorithms. The evaluation of a prototypical implementation shows that those aspects lead to up to two orders of magnitude speedup in algorithm runtime, when exploited. In our experiments, the improved dataflow system is highly competitive with specialized systems while maintaining a transparent and unified data flow abstraction. Part 3 - A Taxonomy of Platforms for Analytics on Big Data, Thomas Bodner Abstract: Within the past few years, industrial and academic organizations designed a wealth of systems for data-intensive analytics including MapReduce, SCOPE/Dryad, ASTERIX, Stratosphere, Spark, and many others. These systems are being applied to new applications from diverse domains other than (traditional) relational OLAP, making it difficult to understand the tradeoffs between them and the workloads for which they were built. We present a taxonomy of existing system stacks based on their architectural components and the design choices made related to data processing and programmability to sort this space. We further demonstrate a web repository for sharing Big Data analytics platform information and use cases. The repository enables researchers and practitioners to store and retrieve data and queries for their use case, and to easily reproduce experiments from others on different platforms, simplifying comparisons.
Views: 384 Microsoft Research
How to : Do data mining
Do data mining One way that some corporations keep ahead of their competition is to do data mining. Businesses derive useful information from huge databases through statistical analysis. Applications of this mathematical algorithm based analysis tool are in the areas of product analysis, consumer research marketing, e-Commerce, stock investment trend and many more. Relational database mining, web mining, text mining, audio and video mining, and social networks mining are some types of data mining. You can relate data mining to geology in the sense that in geology you search for specific minerals (for example gold or lead), while a statistical data miner uses various tools to find useful information from a wide database. It is a way of extracting data from very large and sometimes complex databases to find patterns or trends that a company can use to further their business. Data mining is a labor intensive job wherein a lot of data has to be collected and analyzed. Outsourcing data mining jobs may be more beneficial to companies who do not have the time or manpower to invest in this endeavor. The outsourcing company will take care of collecting the needed data and organizing the data in a well mapped database so that they can easily filter or extract the required information for analysis. But if you have the resources, you can also use a variety of data mining programs out there. Some data mining software are SAS Enterprise Miner, DataDetective, Statistical Data Miner, Statistica, and Weka. You can read more about data mining on the Internet. But just to give you an idea, below are the steps in performing data mining: Define the objectives. This step is basically identifying why you need to perform data mining. What problem brought about a perceived data mining solution and what are the objectives for this project? Gather and organize the data. The bulk of the work in data mining is data gathering and exploring. Data has to be organized in an efficient and effective way for you to be able to process the information properly. Select the data-mining task. There are four basic data mining techniques: classification, regression, clustering and association rule. Choose the ones appropriate to your objectives. Modeling. This is when you actually perform the data mining procedure. Search for patterns in the database by applying your selected data mining techniques in order to create models. Data interpretation and validation. After the actual data mining task, the data gathered is now interpreted, validated, transformed and visualized using statistical techniques. Data deployment. This step can involve a report that is generated showing the patterns found in the data mining activity or the use of the data model on a larger group of data for further analysis. Data mining is an iterative process so you may have to go through several of the steps above a number of times until the results you derive answer your objectives. There was a time when data mining was not widely used by businesses. Now, public and private companies and organizations find data mining an invaluable way for them to keep up and even get ahead of their competitors. Businesses are now able to monitor the kind of customers their products cater to and what their customersโ€™ buying behaviors are. The information mined and modeled from various types of databases is used for competition analysis, market research, economic trending, consumer behavior, industry research, geographical information analysis and so on. Even the FBI and other law enforcement groups use data mining techniques.
Developer Data Scientist โ€“ New Analytics Driven Apps Using Azure Databricks & Apache Spark | B116
This session gives an introduction to machine learning for developers who are new to data science, and it shows how to build end-to-end MLlib Pipelines in Apache Spark. It provides example code to personalize recommendations, score inbound leads, or do natural language processing in Scala and Python. See how to productionize machine learning pipelines to create richer, more useful applications.
Remco van der Hofstad - The Structure of Complex Networks: Scale-Free and Small-World Random Graphs
Abstract: Many phenomena in the real world can be phrased in terms of networks. Examples include the World-Wide Web, social interactions and Internet, but also the interaction patterns between proteins, food webs and citation networks. Many large-scale networks have, despite their diversity in backgrounds, surprisingly much in common. Many of these networks are small worlds, in the sense that one requires few links to hop between pairs of vertices. Also the variability of the number of connections between elements tends to be enormous, which is related to the scale-free phenomenon. In this lecture for a broad audience, we describe a few real-world networks and some of their empirical properties. We also describe the effectiveness of abstract network modeling in terms of graphs and how real-world networks can be modeled, as well as how these models help us to give sense to the empirical findings. We continue by discussing some random graph models for real-world networks and their properties, as well as their merits and flaws as network models. We conclude by discussing the implications of some of the empirical findings on information diffusion and competition on such networks. We assume no prior knowledge in graph theory, probability or otherwise.
The Spatiotemporal Epidemiological Modeler (short demo)
The video is a short demonstration of the Spatiotemporal Epidemiological Modeler (STEM), an open source Eclipse based application for modeling of infectious diseases. More information can be found at the mail STEM web site, http://www.eclipse.org/stem
Views: 1282 Research-Almaden
Lecture - 35 Data Mining and Knowledge Discovery Part II
Lecture Series on Database Management System by Dr. S. Srinath,IIIT Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 43354 nptelhrd
Scalable In Database Regression Analysis of Large Earth Observation Datasets
EO Open Science 2.0 Poster space 28 Session B2 Marius Appel, University of Muenster
Views: 126 EO Open Science
Understanding Big Data (and not die trying) by Dr Serge Plata (Head of Analytics, Home Retail)
talk given at IMA London branch 1st November 2016 http://www.ima.org.uk/activities/branches/london.cfm.html Abstract: There are so many terms in the analytics industry today, and no one really understands them well: Data science, data analysis (or is it analytics?), data architecture, web analytics, business intelligence, and management information; there are also many platforms from relational databases to Hadoop clusters, and programming languages like python, .NET, C# and F# (sounds like music!); not to mention all the statistical packages like (R, SPSS and STATA) and front end tools (like Pentaho, D3.js or Tableau). But what does this mean? Where do all of these elements fit in the data and analytics profession? In this talk I will explain what they are, how they work and how they interact with each other, but most importantly how businesses use them to take advantage of all these mathematical tools and make data-driven decisions. I will give real life examples on these tools and methods like machine learning, data mining and mathematical modelling and also how platforms are used. Dr. Plata did his doctoral research at Imperial College London before taking up a post as a research fellow at the University of Exeter. Prior to that, he did a degree and masters in mathematics, specializing firstly on differential and algebraic topology and moving then into spectral theory, homeomorphic dynamics and ergodic theory which classically fall into the applied fields like optimisation, game theory, and machine learning. Among other publications, he wrote a book under Peter Lang Publishers titled "Visions of Applied Mathematics" In terms of mathematical applications, Dr. Plata has extensive experience building, and developing analytics programmes as well as leading data projects and data science teams from FTSE100 companies to technology SMEs. He has worked mainly in the retail space including digital and mobile, pioneering on behaviour analytics, machine learning and big data. He is a fellow of the IMA and he currently heads the 'data science and advanced analytics' team at Home Retail.
Views: 486 IMAmaths
Women in Data Science | Mingyan Liu
Mingyan Liu, Professor of Electrical Engineering and Computer Science (http://web.eecs.umich.edu/~mingyan/) gives the lecture "Confessions of a Pseudo Data Scientist" at the Women in Data Science Conference hosted by MIDAS (http://midas.umich.edu/). Dr. Liuโ€™s research interests include optimal resource allocation, sequential decision theory, incentive design, and performance modeling and analysis, all within the context of communications networks. Her most recent research involves online learning, modeling and mining of large-scale internet measurement data concerning cyber-security, and incentive mechanisms for interdependent security games. For more lectures on demand, visit the Alumni Engagement website: http://www.engin.umich.edu/college/info/alumni/professional-dev/lectures
Learning Rules for Anomaly Detection
Google Tech Talks August 10, 2007 ABSTRACT Anomaly detection has the potential to detect novel attacks, however, keeping the false alarm rate low is a challenging task. We discuss the LERAD algorithm that can learn concise and accurate rules for anomaly detection and demonstrate its effectiveness in network and host datasets. We will also discuss our recent work (KDD 07) on weighting versus pruning during the rule validation. If there is more time, I can also talk about: As mobile devices become more pervasive, we study the problem of spatial-temporal anomaly detection for identifying potential abuse. We discuss the STAD algorithm and show its performance on a cell phone dataset. Credits:...
Views: 4562 GoogleTechTalks
Machine Learning, Knowledge Extraction And Blockchain: The Knowledge Engine
Part One of A Mathematical Theory of Knowledge: The Knowledge Engine - Machine Learning, Knowledge Extraction And Blockchain Live at The Humboldt Institut fรผr Internet und Gesellschaft for the Blockchain for Science Hackathon. In cooperation with The Living Knowledge Network Foundation. Using mathematics such as algebras, information theory, graph theory, homomorphic encryption, algebraic information theory, domain theory, local computation, Markov Trees, linear algebra, many types of machine learning algorithms, fuzzy logic, many of which overlap, and even blockchain like structures, it can be shown that knowledge can be defined using three measures and extracted from datasets or other recorded observations, when a measure preserving mapping is found or an approximation thereof to form a type of informational compression which can be defined as knowledge. The first lecture on this topic introduces the concept of a decentralized, trusted, public/private knowledge engine which makes use of the aforementioned methods to classify and extract knowledge and link the granules of knowledge together with inferred causality, creating a knowledge base that can be stored and processed on a blockchain like structure. Many previous and current machine learning algorithms can be improved upon and even shown to be equivalent using a mathematical theory of knowledge. Thus more computational expensive methods of machine learning can be avoided, especially once any possible local computation is factored in, and algorithms that still have to be run that are more computational expensive would only have to be run once before the knowledge could be extracted as a measure preserving mapping. This can apply to algorithms such as clustering algorithms, or neural networking such as support vector machines and even deep learning.
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 80366 Dr. Daniel Soper
N-Gram And Stop Words In Artificial Intelligence Explained In Hindi
๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
Views: 4999 5 Minutes Engineering
Running Large Graph Algorithms: Evaluation of Current State-Of-the-Art and Lessons Learned
Google Tech Talk February 11, 2010 ABSTRACT Presented by Dr. Andy Yoo, Lawrence Livermore National Laboratory. Graphs have gained a lot of attention in recent years and have been a focal point in many emerging disciplines such as web mining, computational biology, social network analysis, and national security, just to name a few. These so-called scale-free graphs in the real world have very complex structure and their sizes already have reached unprecedented scale. Furthermore, most of the popular graph algorithms are computationally very expensive, making scalable graph analysis even more challenging. To scale these graph algorithms, which have different run-time characteristics and resource requirements than traditional scientific and engineering applications, we may have to adopt vastly different computing techniques than the current state-of-art. In this talk, I will discuss some of the findings from our studies on the performance and scalability of graph algorithms on various computing environments at LLNL, hoping to shed some light on the challenges in scaling large graph algorithms. Andy Yoo is a computer scientist in the Center for Applied Scientific Computing (CASC). His current research interests are scalable graph algorithms, high performance computing, large-scale data management, and performance evaluation. He has worked on the large graph problems since 2004. In 2005, he developed a scalable graph search algorithm and demonstrated it by searching a graph with billions of edges on IBM BlueGene/L, then the largest and fastest supercomputer. Andy was nominated for 2005 Gordon Bell award for this work. He is currently working on finding right combination of architecture, systems, and programming model to run large graph algorithms. Andy earned his Ph.D. degree in Computer Science and Engineering from the Pennsylvania State University in 1998. He joined LLNL in 1998. Andy is a member of the ACM, IEEE and the IEEE Computer Society, and SIAM.
Views: 19052 GoogleTechTalks
Effective and Ef๏ฌcient Clustering Methods for Correlated Probabilistic Graphs
Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training...... ---------------------------------------------------------------- JAVA & .NET PROJECTS: Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc., EMBEDDED SYSTEMS: Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc., ROBOTICS PROJECTS: Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc., MOBILE APPLICATION (ANDROID & J2ME): Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc., MECHANICAL PROJECTS: Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc., CONTACT US: ECWAY TECHNOLOGIES 15/1 Sathiyamoorthi Nagar, 2nd Cross, Thanthonimalai(Opp To Govt. Arts College) Karur-639 005. TamilNadu , India. Cell: +91 9894917187. Website: www.ecwayprojects.com | www.ecwaytechnologies.com Mail to: [email protected]
Views: 151 Ecway Karur
Matrix Multiplication with solved example in Hindi || BDA || Big Data Analytics ||
The Following is the video explains the Matrix Multiplication One step using Map Reduce And the tricks to solve the example. Other links :- Hadoop introduction :- https://youtu.be/wKBzuZzsaNg Son Algorithm :- https://youtu.be/YwYzYWLK--s Map reduce and combiners :- https://youtu.be/MP4Ya0le6C8 Big data analytics introduction :- https://youtu.be/r9GbSH6Uh9A
Making Scatter Plots/Trendlines in Excel
The title says it all! Check out my Channel at www.burkeyacademy.com for more videos on Statistics and Economics. If you like what I am doing, please consider supporting this effort on Patreon or buy me a cookie through PayPal! https://www.patreon.com/burkeyacademy http://paypal.me/BurkeyAcademy www.burkeyacademy.com
Views: 156374 BurkeyAcademy
Ontological Logical Benefits
Why would you suggest that by using logic and reasoning, it provides a better argument for the existence of God than using empirical evidence?
Views: 111 David Webster
Evaluating Similarity Measures: A Large-Scale Study in Orkut Social Network
Google TechTalk June 21, 2006 Ellen Spertus is a Software Engineer at Google and an Associate Professor of Computer Science at Mills College, where she directs the graduate program in Interdisciplinary Computer Science. She earned her bachelor's, master's, and doctoral degrees from MIT, and has done research in parallel computing, text classification, information retrieval, and online communities. She is also known for her work on women and computing and various odd adventures, which have led to write-ups in The Weekly World News and other fine publications. ABSTRACT As online information services grow, so does the need and opportunity for automated tools to help users find information of interest to them. One such method is collaborative filtering, which makes recommendations to users based on their collective past behavior. We performed an extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network, as well as observing interesting social issues that arise in recommending communities within a real social network. Google engEDU
Views: 277 GoogleTalksArchive
Ontology Development & Apps for Clinical & Biological Adverse Event Data Integration & Analysis
Yongqun ''Oliver" He, DVN, Ph.D. Dept. of Microbiology and Immunology Center for Computational Medicine and Bioinformatics and Comprehensive Cancer Center University of Michigan Medical Shoal
Views: 329 UTHealth SBMI
Amit Kapoor - Visualising Multi Dimensional Data
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. Standard 2D/3D Approaches Aesthetics e.g. Color, Size, Shape Small Multiples e.g. Trellis / Facets Matrices Views e.g. SPLOMs 3D Scatterplot Geometric Transformation Approaches Alternate Coordinates e.g. Parallel, Star Projections e.g. Dimensionality Reduction Tablelens Glyph based Approaches Star glyphs Stick Figures Pixel based Approaches Pixel bar charts Space filling curves Stacked based Approaches Dimensional Stacking Hierarchical Axis Treemaps The talk will also explore the role of interaction approaches to enhance our ability to visually explore the multi dimensional data. Interactive Approaches Navigation - Pan, Zoom, Scale, Rotate Selection & Annotation Filtering - Highlighting, Brushing and Linking Layering Dynamic Queries
Views: 3090 HasGeek TV
Excel at Data Mining โ€“ Creating and Reading a Classification Matrix
In this video, Billy Decker of StatSlice Systems shows you how to create and read a Classification Matrix in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a Classification Matrix based on a mining structure with all of its associated models that we have created previously. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 You will also need to attach the AdventureworksDW2012 data file to SQL Server which can be downloaded here: http://msftdbprodsamples.codeplex.com/releases/view/55330 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server with Analysis Services to which you have access rights.
Views: 4120 StatSlice Systems
Graph Analytics using Deep Learning
Video Upload for Group C8 (Sarang Karpate, Gabriel Ryan) Advanced Big Data Analytics
Views: 427 Sarang Karpate
Approximate Inference Techniques for Identity Uncertainty
Many interesting tasks, such as vehicle tracking, data association, and mapping, involve reasoning about the objects present in a domain. However, the observations on which this reasoning is to be based frequently fail to explicitly describe these objects' identities, properties, or even their number, and may in addition be noisy or nondeterministic. When this is the case, identifying the set of objects present becomes an important aspect of the whole task. My talk will discuss how this task can be handled using methods that add relational elements to probabilistic representations; specifically, to directed and undirected graphical models. A recurring problem with such graphical models, ones that express uncertainty over the set of objects in existence as well as over their properties, is that they are highly connected, which makes exact inference and learning highly intractable. Fortunately, many of the connections become irrelevant given a specific set of objects, so the problem can be overcome with the help of approximate techniques based on sampling or stochastic search over the set of objects. I will describe such techniques, and explain how they can be applied to citation matching and topological map construction. In both cases, I will demonstrate that the ability to reason about the properties of the objects responsible for the observations (papers and authors, or locations) can improve a system's ability to identify these objects.
Views: 52 Microsoft Research
Belajar Python : Dasar Python
Seri Belajar Python Data Science dan Machine Learning Seri 1 Belajar List, Tuple dan Dictionary dalam Python Belajat Fungsi dalam Python Belajar Loop dan Boolean dalam Python DataScience.zip =
Views: 1197 Ageng Rikhmawan
Cross Media Learning Management System
The solution for distance and continuous learning, based on social media technologies. Social and collaborative Portal: โ€ข collaborative environment โ€ข networking โ€ข blogs and forums โ€ข suggestions and recommendations Learning Management system โ€ข Cross media e-learning โ€ข collective analysis โ€ข user behaviour analysis Intelligent content model โ€ข complex interactive content โ€ข multimedia annotations โ€ข personal collection and playlist Access with different devices โ€ข everywhere you are โ€ข whenever you like โ€ข with any device real application on: http://www.eclap.eu -- http://mobmed.axmedis.org Innovation Selected "Italia degli innovatori" Italy of innovators: http://www.disit.dsi.unifi.it/projects.html
Views: 105 Paolo Nesi
Prosopography and Computer Ontologies: the 'factoid' model and CIDOC-CRM - Michele Pasin
Michele Pasin, Research Associate, Kings College, London Presented at "Representing Knowledge in the Digital Humanities", University of Kansas, September 24, 2011 Institute for Digital Research in the Humanities: http://idrh.ku.edu Abstract: Structured Prosopography provides a formal model for representing prosopography: a branch of historical research that traditionally has focused on the identification of people that appear in historical sources. Pre-digital print prosopographies, such as Martindale 1992, presented its materials as narrative articles about the individuals it contains. Since the 1990s, KCL's Department of Digital Humanities (formerly known as Center for Computing in the Humanities) has been involved in the development of structured prosopographical databases, and has had direct involvement in Prosopographies of the Byzantine World (PBE and PBW), Anglo-Saxon England (PASE), Medieval Scotland (PoMS) and now more generally northern Britain ("Breaking of Britain": BoB), and is currently in discussions about others. DDH has been involved in the development of a general "factoid-oriented" model of structure that although downplaying or eliminating narratives about people, has to a large extent served the needs of these various projects quite well.
Authors@Google: Ben Shneiderman
"Analyzing Social Media Networks with NodeXL" When information visualization is smoothly integrated with statistical techniques users can make important discoveries and bold decisions. Our 20-year history in coupling direct manipulation principles with dynamic queries, coordinated multiple windows, tree-maps, time-box selectors, and other innovations has produced academic and commercial success stories such as www.Spotfire.com and www.cs.umd.edu/hcil/treemap-history. Now we've turned to the difficult problem of network analysis and visualization. The free, open-source NodeXL (www.codeplex.com/nodexl) demonstrates novel approaches to importing network data (email, website, Facebook, Twitter, Flickr, etc.), applying metrics, performing clustering, and then giving rich controls over network layouts to support exploration and presentation. BEN SHNEIDERMAN (http://www.cs.umd.edu/~ben) is a Professor in the Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Laboratory (http://www.cs.umd.edu/hcil/) at the University of Maryland. He was elected as a Fellow of the Association for Computing (ACM) in 1997, a Fellow of the American Association for the Advancement of Science (AAAS) in 2001, and a Member of the National Academy of Engineering in 2010. He received the ACM SIGCHI Lifetime Achievement Award in 2001.
Views: 4141 Talks at Google
Data processing: Achieving optimal performance automatically (Google Cloud Next '17)
Performance is great, but what's even better than finely-tuned, benchmark-optimized systems? Performance that's geared towards your exact needs. In this video, Jelena Pjesivac-Grbovic demonstrates how Google Cloud Platform's (GCP) data processing provides best-in-class performance out of the box, with no need for fine-tuning or manual optimization. Missed the conference? Watch all the talks here: https://goo.gl/c1Vs3h Watch more talks about Big Data & Machine Learning here: https://goo.gl/OcqI9k
Views: 1033 Google Cloud Platform
HCDF: A Hybrid Community Discovery Framework
Google Tech Talk March 11, 2010 ABSTRACT Presented by Tina Eliassi-Rad. We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework ), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structure as defined by Variation of Information (an entropy-based distance metric). Dr. Tina Eliassi-Rad, Lawrence Livermore National Laboratory http://people.llnl.gov/eliassirad1 Tina Eliassi-Rad (http://eliassi.org) is a computer scientist and principal investigator at the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. She will join the faculty at the Department of Computer Science at Rutgers University in Fall 2010. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research interests include data mining, machine learning, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. She serves as an action editor for the Data Mining and Knowledge Discovery Journal.
Views: 4358 GoogleTechTalks