Search results “Cluster detection algorithm in data mining”

What is clustering
Partitioning a data into subclasses.
Grouping similar objects.
Partitioning the data based on similarity.
Eg:Library.
Clustering Types
Partitioning Method
Hierarchical Method
Agglomerative Method
Divisive Method
Density Based Method
Model based Method
Constraint based Method
These are clustering Methods or types.
Clustering Algorithms,Clustering Applications and Examples are also Explained.

Views: 82037
IT Miner - Tutorials,GK & Facts

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how to draw star schema slowflake schema and fact constelation
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decision tree with solved example
K mean clustering algorithm
Introduction to data mining and architecture
Naive bayes classifier
Apriori Algorithm
Agglomerative clustering algorithmn
KDD in data mining
ETL process
FP TREE Algorithm
Decision tree

Views: 260886
Last moment tuitions

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This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4
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#kmeans #clusteranalysis #clustering #datascience #machinelearning
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Views: 55633
edureka!

Supervised and unsupervised learning algorithms

Views: 59151
Nathan Kutz

Full lecture: http://bit.ly/K-means
The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.

Views: 448033
Victor Lavrenko

Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 8587
nptelhrd

Introduction
Road and accidents are uncertain and unsure incidents. In today’s world, traffic is increasing at a huge rate which leads to a large numbers of road accidents.
Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident.
Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident.
In this project, Apriori algorithm clubbed with Kmeans Clustering is used to analyse the road accidents factors
Kmeans Algorithm
The algorithm is composed of the following steps:
It randomly chooses K points from the data set.
Then it assigns each point to the group with closest centroid.
It again recalculates the centroids.
Assign each point to closest centroid.
The process repeats until there is no change in the position of centroids.
Apriori Algorithm
Apriori involves frequent item-sets, which is a set of items appearing together in the given number of database records meeting the user-specified threshold.
Apriori uses a bottom-up search method that creates every single frequent item-set.
This means that to produce a frequent item-set of length; it must produce all of its subsets as need to be frequent.
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Views: 380
E2MATRIX RESEARCH LAB

What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2]
In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3]
Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.

Views: 4921
The Audiopedia

by Batool Arhamna Haider

Views: 27180
Kanza Batool Haider

MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING

Views: 290
M R Dhandhukia

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.
Code for this video:
https://github.com/llSourcell/k_means_clustering
Please Subscribe! And like. And comment. That's what keeps me going.
More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html
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Views: 79748
Siraj Raval

Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/

Views: 8915
Microsoft Research

Java Swing based OPTICS clustering algorithm simulation.
OPTICS is improved version of DBSCAN algorithm.
Source code is browsable on:
https://[email protected]/boetsid/public.git

Views: 6962
General Research

Introduction
Data Mining deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases.
Crime analyses is one of the important application of data mining. Data mining contains many tasks and techniques including Classification, Association, Clustering, Prediction each of them has its own importance and applications
It can help the analysts to identify crimes faster and help to make faster decisions.
The main objective of crime analysis is to find the meaningful information from large amount of data and disseminates this information to officers and investigators in the field to assist in their efforts to apprehend criminals and suppress criminal activity.
In this project, Kmeans Clustering is used for crime data analysis.
Kmeans Algorithm
The algorithm is composed of the following steps:
It randomly chooses K points from the data set.
Then it assigns each point to the group with closest centroid.
It again recalculates the centroids.
Assign each point to closest centroid.
The process repeats until there is no change in the position of centroids.
Example of KMEANS Algorithm
Let’s imagine we have 5 objects (say 5 people) and for each of them we know two features (height and weight). We want to group them into k=2 clusters.
Our dataset will look like this:
First of all, we have to initialize the value of the centroids for our clusters. For instance, let’s choose Person 2 and Person 3 as the two centroids c1 and c2, so that c1=(120,32) and c2=(113,33).
Now we compute the Euclidean distance between each of the two centroids and each point in the data.

Views: 415
E2MATRIX RESEARCH LAB

Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 19695
nptelhrd

Author:
Shalmoli Gupta, Department of Computer Science, University of Illinois at Urbana-Champaign
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

Views: 1277
KDD2016 video

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
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Views: 9752
Cognitive Class

Tselil Schramm (Simons Institute, UC Berkeley)
One of the greatest advantages of representing data with graphs is access to generic algorithms for analytic tasks, such as clustering. In this talk I will describe some popular graph clustering algorithms, and explain why they are well-motivated from a theoretical perspective.
-------------------
References from the Whiteboard:
Ng, Andrew Y., Michael I. Jordan, and Yair Weiss. "On spectral
clustering: Analysis and an algorithm." Advances in neural information
processing systems. 2002.
Lee, James R., Shayan Oveis Gharan, and Luca Trevisan. "Multiway
spectral partitioning and higher-order cheeger inequalities." Journal
of the ACM (JACM) 61.6 (2014): 37.
-------------------
Additional Resources:
In my explanation of the spectral embedding I roughly follow the exposition from the lectures of Dan Spielman (http://www.cs.yale.edu/homes/spielman/561/), focusing on the content in lecture 2. Lecture 1 also contains some additional striking examples of graphs and their spectral embeddings.
I also make some imprecise statements about the relationship between the spectral embedding and the minimum-energy configurations of a mass-spring system. The connection is discussed more precisely here (https://www.simonsfoundation.org/2012/04/24/network-solutions/).
License: CC BY-NC-SA 4.0
- https://creativecommons.org/licenses/by-nc-sa/4.0/

Views: 4603
GraphXD: Graphs Across Domains

Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.

Views: 10719
Ben Rodick

Machine Learning #74 CURE Algorithm | Clustering
In this lecture of macghine learning we are going to see CURE Algorithm for clustering with example. A new scalable algorithm called CURE is introduced, which uses random sampling and partitioning to reliably find clusters of arbitrary shape and size. CURE algorithm clusters a random sample of the database in an agglomerative fashion, dynamically updating a constant number c of well-scattered points. CURE divides the random sample into partitions which are pre-clustered independently, then the partially-clustered sample is clustered further by the agglomerative algorithm. A new algorithm for detecting arbitrarily-shaped clusters at large-scale is presented and named CURE, for “Clustering Using Representatives”.
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Views: 581
Xoviabcs

PyData NYC 2015
Clustering data into similar groups is a fundamental task in data science. Probability density-based clustering has several advantages over popular parametric methods like K-Means, but practical usage of density-based methods has lagged for computational reasons. I will discuss recent algorithmic advances that are making density-based clustering practical for larger datasets.
Clustering data into similar groups is a fundamental task in data science applications such as exploratory data analysis, market segmentation, and outlier detection. Density-based clustering methods are based on the intuition that clusters are regions where many data points lie near each other, surrounded by regions without much data.
Density-based methods typically have several important advantages over popular model-based methods like K-Means: they do not require users to know the number of clusters in advance, they recover clusters with more flexible shapes, and they automatically detect outliers. On the other hand, density-based clustering tends to be more computationally expensive than parametric methods, so density-based methods have not seen the same level of adoption by data scientists.
Recent computational advances are changing this picture. I will talk about two density-based methods and how new Python implementations are making them more useful for larger datasets. DBSCAN is by far the most popular density-based clustering method. A new implementation in Dato's GraphLab Create machine learning package dramatically speeds up DBSCAN computation by taking advantage of GraphLab Create's multi-threaded architecture and using an algorithm based on the connected components of a similarity graph.
The density Level Set Tree is a method first proposed theoretically by Chaudhuri and Dasgupta in 2010 as a way to represent a probability density function hierarchically, enabling users to use all density levels simultaneous, rather than choosing a specific level as with DBSCAN. The Python package DeBaCl implements a modification of this method and a tool for interactively visualizing the cluster hierarchy.
Slides available here: https://speakerdeck.com/papayawarrior/density-based-clustering-in-python
Notebooks: http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_dbscan.ipynb
http://nbviewer.ipython.org/github/papayawarrior/public_talks/blob/master/pydata_nyc_DeBaCl.ipynb

Views: 12474
PyData

Pattern Recognition by Prof. C.A. Murthy & Prof. Sukhendu Das,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 10370
nptelhrd

This video discusses about outliers and its possible cause.

Views: 14229
Gourab Nath

Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining
Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation
http://www.studyyaar.com/index.php/module-video/watch/53-data-mining

Views: 82815
StudyYaar.com

What is CLUSTER ANALYSIS? What does CLUSTER ANALYSIS mean? CLUSTER ANALYSIS meaning - CLUSTER ANALYSIS definition - CLUSTER ANALYSIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify data preprocessing and model parameters until the result achieves the desired properties.
Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek ß????? "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.
Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.

Views: 5906
The Audiopedia

Full lecture: http://bit.ly/K-means
Clustering can be used to represent natural images for the purpose of object detection or image tagging. We partition an image using a rectangular grid, compute a feature vector for each cell, and use K-means to assign each vector to a cluster. The clusters can then be used as discrete attributes for representing the entire image (this is known as a bag-of-visual-terms representation).

Views: 10376
Victor Lavrenko

Cluster is a unsupervised learning algorithm used for modelling unlabeled data.
Supervised learning: discover patterns in the data that relate data attributes with a target (class) attribute.
These patterns are then utilized to predict the values of the target attribute in future data instances.
Unsupervised learning: The data have no target attribute.
We want to explore the data to find some intrinsic structures in them.
Clustering is a technique for finding similarity groups in data, called clusters. I.e.,
it groups data instances that are similar to (near) each other in one cluster and data instances that are very different (far away) from each other into different clusters.
Clustering is often called an unsupervised learning task
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Views: 693
Analytics University

Learn K-Means clustering in very simple way

Views: 9079
Red Apple Tutorials

Blog: http://code-ai.mk/
K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
- The Algorithm
K-Means starts by randomly defining k centroids. From there, it works in iterative (repetitive) steps to perform two tasks:
Assign each data point to the closest corresponding centroid, using the standard Euclidean distance. In layman’s terms: the straight-line distance between the data point and the centroid.
For each centroid, calculate the mean of the values of all the points belonging to it. The mean value becomes the new value of the centroid.
Once step 2 is complete, all of the centroids have new values that correspond to the means of all of their corresponding points. These new points are put through steps one and two producing yet another set of centroid values. This process is repeated over and over until there is no change in the centroid values, meaning that they have been accurately grouped. Or, the process can be stopped when a previously determined maximum number of steps has been met.
This application is written in C# with my own implementation of K-Means algorithm. The work of the algorithm is displayed in Windows Forms and it is enough to see it in action one iteration at a time.
Let me know if you want the source code. Please remember this implementation is intended for studying purpose.

Views: 230
Vanco Pavlevski

.
Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
.

Views: 22061
Artificial Intelligence - All in One

This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

Views: 13191
Udacity

In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy

Views: 362716
Thales Sehn Körting

How to solve k-means clustering algorithm using centroid technique.
2 basic examples of k-means algorithm.
music credits:
1. Death_note
2. Rishhsome_vines

Views: 80
CSExpert

In this tutorial about python for data science, you will learn about DBSCAN (Density-based spatial clustering of applications with noise) Clustering method to identify/ detect outliers in python. you will learn how to use two important DBSCAN model parameters i.e. Eps and min_samples. Environment used for coding is Jupyter notebook. (Anaconda)
This is the 22th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
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Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca
Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Views: 8573
TheEngineeringWorld

Machine Learning #75 Density Based Clustering
Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm
Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE)
Best Programming Courses @ https://goo.gl/MVVDXR
Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if
MATLAB Tutorials @ https://goo.gl/EiPgCF

Views: 2567
Xoviabcs

The scikit learn library for python is a powerful machine learning tool.
K means clustering, which is easily implemented in python, uses geometric distance to create centroids around which our data can fit as clusters.
In the example attached to this article, I view 99 hypothetical patients that are prompted to sync their smart watch healthcare app data with a research team. The data is recorded continuously, but to comply with healthcare regulations, they have to actively synchronize the data. This example works equally well is we consider 99 hypothetical customers responding to a marketing campaign.
In order to prompt them, several reminder campaigns are run each year. In total there are 32 campaigns. Each campaign consists only of one of the following reminders: e-mail, short-message-service, online message, telephone call, pamphlet, or a letter. A record is kept of when they sync their data, as a marker of response to the campaign.
Our goal is to cluster the patients so that we can learn which campaign type they respond to. This can be used to tailor their reminders for the next year.
In the attached video, I show you just how easy this is to accomplish in python. I use the python kernel in a Jupyter notebook. There will also a mention of dimensionality reduction using principal component separation, also done using scikit learn. This is done so that we can view the data as a scatter plot using the plotly library.

Views: 30034
Juan Klopper

Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
I consider first a rather simple intuitive criterion of individual cluster
analysis, the product of the average within-cluster similarity and the number
of elements in it to be maximized, and bring forth its mathematical
properties relating the criterion with high-density subgraphs and spectral
clustering approach. Then I present a simple approximation anomalous
cluster model leading to the criterion and families of very effective ADDI
crisp clustering methods (Mirkin, 1987) and FADDIS fuzzy clustering
methods (Mirkin, Nascimento, 2012); the latter leading to mysteries in the
popular Laplace similarity data normalization.
Then I show that the celebrated square-error k-means clustering criterion
can be equivalently reformulated as of finding a partition consisting of the
anomalous clusters. I will finish with a problem in consensus clustering
to show that it is equivalent to the anomalous similarity clustering and
present experimental results of the superiority of this approach over
competition.

Views: 827
Компьютерные науки

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-313488098/m-674518790
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 66279
Udacity

Examines the way a k-means cluster analysis can be conducted in RapidMinder

Views: 44293
Gregory Fulkerson

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Views: 9249
Artificial Intelligence - All in One

[http://bit.ly/s-link] Agglomerative clustering guarantees that similar instances end up in the same cluster. We start by having each instance being in its own singleton cluster, then iteratively do the following steps: (1) find a pair or most similar clusters and (2) merge them into a single cluster. The result is a tree structure called the dendrogram.

Views: 91568
Victor Lavrenko

In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com

Views: 186407
Influxity

Australian Weather Data:
http://www.bom.gov.au/climate/dwo/

Views: 3027
ritvikmath

Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can
offer a richer representation by suggesting the potential group
structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a
parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of
single-linkage clustering algorithm due to its natural expression
of iterative process. Our algorithm can be deployed easily in
Amazon’s cloud environment. And a thorough performance
evaluation in Amazon’s EC2 verifies that the scalability of our
algorithm sustains when the datasets scale up.

Views: 1848
Spark Summit

Clustering Data Streams Based on Shared Density Between Micro-Clusters

Views: 74
CITL Projects

Google Tech Talks
March, 20 2008
ABSTRACT
Clustering is the task of finding groups in data. While traditional clustering algorithms typically measure similarity between objects by considering all attributes/features/dimensions of data objects, projected clustering algorithms attempt to find clusters that may exist only in subspaces, i.e., subsets of attributes. The problem of finding projected clusters is motivated by the fact that in high-dimensional data notions of similarity become less and less meaningful as the dimensionality increases, and meaningful clusters may only exist in smaller subspaces - possibly different for different clusters.
In this talk, I will briefly discuss some prominent approaches to projected clustering, and present a particular projected clustering algorithm P3C, which we have proposed recently, in more detail. P3C does not require many (and often difficult to set) parameter values, and can, under certain conditions (which it shares with most of the approaches proposed in the literature so far), discover the true number of projected clusters. P3C is effective in detecting very low-dimensional projected clusters embedded in high dimensional spaces. P3C is also one of the few projected clustering algorithms that can be extended to deal with categorical data.
Please send me an email (drafiei) if you want to meet the speaker.
Speaker: Jörg Sander
Jörg Sander is currently an Associate Professor at the University of Alberta, Canada. He received his MS in Computer Science in 1996 and his PhD in Computer Science in 1998, both from the University of Munich, Germany. He authored more than 30 papers in international conferences and journals. His current research interests include spatial and spatio-temporal databases, as well as knowledge discovery in databases, especially clustering and data mining in spatial and high-dimensional data sets.

Views: 6097
GoogleTechTalks

Views: 6327
Audimation Services

In this video, you will learn how to perform K Means Clustering using R. Clustering is an unsupervised learning algorithm.
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Analytics University

Supervised and unsupervised learning algorithms

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Nathan Kutz

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