Example: Document Clustering. Clustering: Definition. Notion of a Cluster can be Ambiguous. Types of Clusterings. Hierarchical Clustering


 Thomasine Mason
 1 years ago
 Views:
Transcription
1 Overview Prognostic Models and Data Mining in Medicine, part I Cluster Analsis What is Cluster Analsis? KMeans Clustering Hierarchical Clustering Cluster Validit Eample: Microarra data analsis 6 Summar Includes material from:  Tan, Steinbach, Kumar: Introduction to Data Mining  Witten & Frank: Data Mining Practical Machine Learning Tools and Techniques Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis What is Cluster Analsis? () Finding groups of items that are similar Clustering is unsupervised The class of an eample is not known Success often measured subjectivel What is Cluster Analsis? Sepal length 9 Sepal width Petal length Petal width Tpe Iris setosa Iris setosa 7 7 Iris versicolor 6 Iris versicolor 6 6 Iris virginica Iris virginica Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis What is Cluster Analsis? () Applications of Cluster Analsis Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups Intracluster distances are minimized Intercluster distances are maimized Understanding Group related documents for browsing, group genes and proteins that have similar functionalit, or group stocks with similar price fluctuations Discovered Clusters AppliedMatlDOWN,BaNetworkDown,COMDOWN, CabletronSsDOWN,CISCODOWN,HPDOWN, DSCCommDOWN,INTELDOWN,LSILogicDOWN, MicronTechDOWN,TeasInstDown,TellabsIncDown, NatlSemiconductDOWN,OraclDOWN,SGIDOWN, SunDOWN AppleCompDOWN,AutodeskDOWN,DECDOWN, ADVMicroDeviceDOWN,AndrewCorpDOWN, ComputerAssocDOWN,CircuitCitDOWN, CompaqDOWN, EMCCorpDOWN, GenInstDOWN, MotorolaDOWN,MicrosoftDOWN,ScientificAtlDOWN FannieMaeDOWN,FedHomeLoanDOWN, MBNACorpDOWN,MorganStanleDOWN BakerHughesUP,DresserIndsUP,HalliburtonHLDUP, LouisianaLandUP,PhillipsPetroUP,UnocalUP, SchlumbergerUP Industr Group TechnologDOWN TechnologDOWN FinancialDOWN OilUP Summarization Reduce the size of large data sets Clustering precipitation in Australia Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6
2 Eample: Document Clustering Goal: To find groups of documents that are similar to each other based on the important terms appearing in them Approach: Identif frequentl occurring terms in the documents Define a similarit measure based on the frequencies of different terms Use it to cluster Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents Clustering: Definition Given a set of data points, each having a set of attributes, and a similarit measure among them, find clusters such that Data points in one cluster are more similar to one another Data points in separate clusters are less similar to one another Similarit Measures: Euclidean Distance if attributes are continuous Other Problemspecific Measures Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8 Notion of a Cluster can be Ambiguous Tpes of Clusterings How man clusters? Si Clusters A clustering is a set of clusters Important distinction between hierarchical and partitional sets of clusters Partitional Clustering A division of data objects into nonoverlapping subsets Hierarchical clustering A set of nested partitional clusterings, organized in a tree Two Clusters Four Clusters Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis Partitional Clustering Hierarchical Clustering p p p p p p p p Traditional Hierarchical Clustering Traditional Dendrogram p p p A Partitional Clustering p Nontraditional Hierarchical Clustering p p p p Nontraditional Dendrogram Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis
3 Clustering Algorithms Kmeans clustering Hierarchical clustering Densitbased clustering Kmeans K Clustering Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis Kmeans Clustering Kmeans Clustering Algorithm Partitional clustering method where each cluster C i has a representative point m i (called centroid) Number of clusters, K, is specified in advance Objective: find a set of clusters C,, C k that minimizes the Sum of Squared Errors (SSE): SSE = i= Ci Other evaluation functions are possible K d( m, ) With this evaluation function, centroid m i will be the geometric mean of cluster C i i Central concept is proimit between points Intermediate and final centroids need not be data points (and tpicall the are not) Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6 Eample Kmeans Clustering Details Iteration Initial centroids are often chosen randoml Clusters produced var from one run to another The centroid is (usuall) the mean of the points in the cluster Closeness is measured b Euclidean distance, cosine similarit, correlation, etc Converge is guaranteed for common similarit measures mentioned above Most of the convergence happens in the first few iterations Often the stopping condition is changed to Until relativel few points change clusters Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8
4 The Influence of Initial Centroids Influence of Initial Centroids Kmeans clustering performs a greed search procedure, starting from the initial centroids The search algorithms gets easil stuck in local minima As a result, the final clustering highl depends on the initial centroids Iteration Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis Problems with Selecting Initial Clusters Eample () If there are K real clusters then the chance of selecting one centroid from each cluster is small Chance is relativel small when K is large If clusters are the same size, n, then 8 6 Iteration For eample, if K =, then P =!/ = 6 Sometimes the initial centroids will readjust themselves in right wa, and sometimes the don t Consider an eample of five pairs of clusters Prognostic Models and Data Mining, part I Cluster Analsis Starting with two initial centroids in one cluster of each pair of clusters Prognostic Models and Data Mining, part I Cluster Analsis Clusters Eample () Solutions to Initial Centroids Problem Iteration Multiple runs Helps, but probabilit is not on our side Select more than k initial centroids and then select among these initial centroids Select most widel separated Appl better search technique Eg genetic algorithm Postprocessing Starting with some pairs of clusters having three initial centroids, while other have onl one Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis
5 Preprocessing and Postprocessing Preprocessing Normalize the data Eliminate outliers Postprocessing Eliminate small clusters that ma represent outliers Split loose clusters (clusters with relativel high SSE) Merge clusters that are close and that have relativel low SSE Limitations of Kmeans Kmeans has problems when clusters are of differing Sizes Densities Nonglobular shapes Kmeans has problems when the data contains outliers Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6 Limitations of Kmeans: Differing Sizes Limitations of Kmeans: Differing Densit Kmeans ( Clusters) Kmeans ( Clusters) Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8 Limitations of Kmeans: Nonglobular Shapes Hierarchical Clustering Kmeans ( Clusters) Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis
6 Hierarchical Clustering Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits 6 Strengths of Hierarchical Clustering Do not have to assume an particular number of clusters An desired number of clusters can be obtained b cutting the dendogram at the proper level The ma correspond to meaningful taonomies Eample in biological sciences (eg, animal kingdom, phlogen reconstruction, ) 6 Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis Tpes of hierarchical Clustering Agglomerative Start with the points as individual clusters At each step, merge the closest pair of clusters until one cluster remains Most popular Divisive Start with one, allinclusive cluster At each step, split a cluster until each cluster contains a single point Agglomerative Clustering Algorithm Basic algorithm: Compute the proimit matri Let each data point be a cluster Repeat Merge the two closest clusters Update the proimit matri 6 Until onl a single cluster remains Central concept is proimit between clusters Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis Starting Situation Start with clusters of individual points and a proimit matri p p p p p p p p p p Proimit Matri Intermediate Situation After some merging steps, we have some clusters C C C C C C C C C C C C Proimit Matri C C C Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6
7 Intermediate Situation After Merging We want to merge the two closest clusters (C and C) and update the proimit matri C C C C C C C C C U C * C C C C C C C C C Proimit Matri C C C C U C C C * * * * * * Proimit Matri C C C U C Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8 How to Define InterCluster Similarit How to Define InterCluster Similarit p p p p p p p p p p Similarit? p p p p p p p p MIN MAX Group Average Distance Between Centroids Other p Proimit Matri MIN MAX Group Average Distance Between Centroids Other p Proimit Matri Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis How to Define InterCluster Similarit How to Define InterCluster Similarit p p p p p p p p p p p p p p p p p p MIN MAX Group Average Distance Between Centroids Other p Proimit Matri MIN MAX Group Average Distance Between Centroids Other p Proimit Matri Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis
8 How to Define InterCluster Similarit Cluster Similarit: MIN or Single Link MIN MAX Group Average Distance Between Centroids Other p p p p p p p p p p Proimit Matri Similarit of two clusters is based on the two most similar (closest) points in the different clusters Determined b one pair of points, ie, b one link in the proimit graph I I I I I I 9 6 I I 7 I I 8 Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis Hierarchical Clustering: MIN Strength of MIN 6 6 Two Clusters Nested Clusters Dendrogram Can handle differing sizes and desities Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6 Limitations of MIN Cluster Similarit: MAX or Complete Linkage Similarit of two clusters is based on the two least similar (most distant) points in the different clusters Determined b all pairs of points in the two clusters Sensitive to noise and outliers Two Clusters I I I I I I 9 6 I I 7 I I 8 Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8
9 Hierarchical Clustering: MAX Strength of MAX 6 6 Two Clusters Nested Clusters Dendrogram Less susceptible to noise and outliers Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis Limitations of MAX Hierarchical Clustering: Problems and Limitations Tends to break large clusters Biased towards globular clusters Two Clusters Once a decision is made to combine two clusters, it cannot be undone Does not directl minimize objective function (eg SSE), as in Kmeans Different schemes have problems with one or more of the following: Sensitivit to noise and outliers Handling different sized clusters and conve shapes Breaking large clusters Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis Cluster Validit Clustering algorithms will alwas come up with a clustering of the data, whether that makes sense or not Eg Kmeans: alwas ields K clusters Cluster Validit We must therefore assess whether the results of a cluster analsis are valid Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis
10 Clusters found in Random Data Subjective validation: eeballing Random One method to evaluate clusterings is eeballing not feasible in highdimensional domains Solution: construct and visualize similarit matri Kmeans Complete Link Prognostic Models and Data Mining, part I Cluster Analsis Prognostic Models and Data Mining, part I Cluster Analsis 6 Using Similarit Matri for Cluster Validation Using Similarit Matri for Cluster Validation Clusters in random data are not so crisp Similarit Similarit 6 8 Kmeans Prognostic Models and Data Mining, part I Cluster Analsis 7 Prognostic Models and Data Mining, part I Cluster Analsis 8 Using Similarit Matri for Cluster Validation Clusters in random data are not so crisp Validation Measures Objective measures to evaluate clusterings are generall preferrable over subjective measures For classification methods we have a variet of objective measures to evaluate how good the results are Eg error rate, sensitivit, specificit, AUC, Brier score, For cluster analsis, the number of available measures is much smaller 6 8 Similarit 6 8 and the are more heavil disputed Complete Link Prognostic Models and Data Mining, part I Cluster Analsis 9 Prognostic Models and Data Mining, part I Cluster Analsis 6
11 What are DNA microarras? Gene E E E Gene Eample: Microarra Analsis Ep Ep Ep Gene N Prognostic Models and Data Mining, part I Cluster Analsis 6 Prognostic Models and Data Mining, part I Cluster Analsis 6 Wh clustering? Eample E E E Gene Gene Gene N E E E Gene N Gene Gene Discover functional relations (similar epression functionall related) Assign function to unknown genes Find out which genes control which other genes Clustering It is easier to look at large blocks of similarl epressed genes The dendogram helps show how closel related epression patterns are A Cholesterol sn B Cell ccle C Immediateearl response D Signaling E Tissue remodeling Prognostic Models and Data Mining, part I Cluster Analsis 6 Prognostic Models and Data Mining, part I Cluster Analsis 6 Summar 6 Summar Cluster analsis is the process of finding groups of similar objects Most popular algorithms: Kmeans clustering Agglomerative Hierarchical Clustering Result will criticall depend on various choices Proimit measure / distance metric Number of clusters (Kmeans) Tpe of linkage (Hierarchical clustering) Cluster validation is problematic Prognostic Models and Data Mining, part I Cluster Analsis 6 Prognostic Models and Data Mining, part I Cluster Analsis 66
Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining /8/ What is Cluster
More informationKMeans Cluster Analysis. Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1
KMeans Cluster Analsis Chapter 3 PPDM Class Tan,Steinbach, Kumar Introduction to Data Mining 4/18/4 1 What is Cluster Analsis? Finding groups of objects such that the objects in a group will be similar
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/4 What is
More informationCluster Analysis: Basic Concepts and Algorithms
Cluster Analsis: Basic Concepts and Algorithms What does it mean clustering? Applications Tpes of clustering Kmeans Intuition Algorithm Choosing initial centroids Bisecting Kmeans Postprocessing Strengths
More informationData Mining Clustering. Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining
Data Mining Clustering Toon Calders Sheets are based on the those provided b Tan, Steinbach, and Kumar. Introduction to Data Mining What is Cluster Analsis? Finding groups of objects such that the objects
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Clustering Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analsis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining b Tan, Steinbach, Kumar Clustering Algorithms Kmeans and its variants Hierarchical clustering
More informationData Mining Clustering (2) Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining
Data Mining Clustering (2) Toon Calders Sheets are based on the those provided by Tan, Steinbach, and Kumar. Introduction to Data Mining Outline Partitional Clustering Distancebased Kmeans, Kmedoids,
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/8/2004 Hierarchical
More informationDATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS
DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS 1 AND ALGORITHMS Chiara Renso KDDLAB ISTI CNR, Pisa, Italy WHAT IS CLUSTER ANALYSIS? Finding groups of objects such that the objects in a group will be similar
More informationClustering. Adrian Groza. Department of Computer Science Technical University of ClujNapoca
Clustering Adrian Groza Department of Computer Science Technical University of ClujNapoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 Kmeans 3 Hierarchical Clustering What is Datamining?
More informationCluster Analysis Overview. Data Mining Techniques: Cluster Analysis. What is Cluster Analysis? What is Cluster Analysis?
Cluster Analsis Overview Data Mining Techniques: Cluster Analsis Mirek Riedewald Man slides based on presentations b Han/Kamber, Tan/Steinbach/Kumar, and Andrew Moore Introduction Foundations: Measuring
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 by Tan, Steinbach, Kumar 1 What is Cluster Analysis? Finding groups of objects such that the objects in a group will
More informationCLASSIFICATION AND CLUSTERING. Anveshi Charuvaka
CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training
More informationFor supervised classification we have a variety of measures to evaluate how good our model is Accuracy, precision, recall
Cluster Validation Cluster Validit For supervised classification we have a variet of measures to evaluate how good our model is Accurac, precision, recall For cluster analsis, the analogous question is
More informationClustering UE 141 Spring 2013
Clustering UE 141 Spring 013 Jing Gao SUNY Buffalo 1 Definition of Clustering Finding groups of obects such that the obects in a group will be similar (or related) to one another and different from (or
More informationClustering. Data Mining. Abraham Otero. Data Mining. Agenda
Clustering 1/46 Agenda Introduction Distance Knearest neighbors Hierarchical clustering Quick reference 2/46 1 Introduction It seems logical that in a new situation we should act in a similar way as in
More informationUnsupervised learning: Clustering
Unsupervised learning: Clustering Salissou Moutari Centre for Statistical Science and Operational Research CenSSOR 17 th September 2013 Unsupervised learning: Clustering 1/52 Outline 1 Introduction What
More informationCluster Analysis. Alison Merikangas Data Analysis Seminar 18 November 2009
Cluster Analysis Alison Merikangas Data Analysis Seminar 18 November 2009 Overview What is cluster analysis? Types of cluster Distance functions Clustering methods Agglomerative Kmeans Densitybased Interpretation
More informationClustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016
Clustering Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 1 Supervised learning vs. unsupervised learning Supervised learning: discover patterns in the data that relate data attributes with
More informationCluster Analysis: Advanced Concepts
Cluster Analysis: Advanced Concepts and dalgorithms Dr. Hui Xiong Rutgers University Introduction to Data Mining 08/06/2006 1 Introduction to Data Mining 08/06/2006 1 Outline Prototypebased Fuzzy cmeans
More informationText Clustering. Clustering
Text Clustering 1 Clustering Partition unlabeled examples into disoint subsets of clusters, such that: Examples within a cluster are very similar Examples in different clusters are very different Discover
More informationCluster Analysis: Basic Concepts and Algorithms
8 Cluster Analysis: Basic Concepts and Algorithms Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should
More informationData Mining: Exploring Data. Lecture Notes for Chapter 3. Introduction to Data Mining
Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 8/05/2005 1 What is data exploration? A preliminary
More informationROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015
ROBERTO BATTITI, MAURO BRUNATO. The LION Way: Machine Learning plus Intelligent Optimization. LIONlab, University of Trento, Italy, Apr 2015 http://intelligentoptimization.org/lionbook Roberto Battiti
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 10 th, 2013 WolfTilo Balke and Kinda El Maarry Institut für Informationssysteme Technische Universität Braunschweig
More informationClustering & Association
Clustering  Overview What is cluster analysis? Grouping data objects based only on information found in the data describing these objects and their relationships Maximize the similarity within objects
More informationData Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining
Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004
More informationData Mining Project Report. Document Clustering. Meryem UzunPer
Data Mining Project Report Document Clustering Meryem UzunPer 504112506 Table of Content Table of Content... 2 1. Project Definition... 3 2. Literature Survey... 3 3. Methods... 4 3.1. Kmeans algorithm...
More informationCluster Analysis. Isabel M. Rodrigues. Lisboa, 2014. Instituto Superior Técnico
Instituto Superior Técnico Lisboa, 2014 Introduction: Cluster analysis What is? Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from
More informationChapter 7. Cluster Analysis
Chapter 7. Cluster Analysis. What is Cluster Analysis?. A Categorization of Major Clustering Methods. Partitioning Methods. Hierarchical Methods 5. DensityBased Methods 6. GridBased Methods 7. ModelBased
More informationData Mining: Exploring Data. Lecture Notes for Chapter 3. Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler
Data Mining: Exploring Data Lecture Notes for Chapter 3 Slides by Tan, Steinbach, Kumar adapted by Michael Hahsler Topics Exploratory Data Analysis Summary Statistics Visualization What is data exploration?
More informationSoSe 2014: MTANI: Big Data Analytics
SoSe 2014: MTANI: Big Data Analytics Lecture 4 21/05/2014 Sead Izberovic Dr. Nikolaos Korfiatis Agenda Recap from the previous session Clustering Introduction Distance mesures Hierarchical Clustering
More informationThe Graph of a Linear Equation
4.1 The Graph of a Linear Equation 4.1 OBJECTIVES 1. Find three ordered pairs for an equation in two variables 2. Graph a line from three points 3. Graph a line b the intercept method 4. Graph a line that
More informationData Mining. Cluster Analysis: Advanced Concepts and Algorithms
Data Mining Cluster Analysis: Advanced Concepts and Algorithms Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 More Clustering Methods Prototypebased clustering Densitybased clustering Graphbased
More informationFig. 1 A typical Knowledge Discovery process [2]
Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Review on Clustering
More informationData Mining: Exploring Data. Lecture Notes for Chapter 3. Introduction to Data Mining
Data Mining: Exploring Data Lecture Notes for Chapter 3 Introduction to Data Mining by Tan, Steinbach, Kumar What is data exploration? A preliminary exploration of the data to better understand its characteristics.
More informationData Mining Cluster Analysis: Advanced Concepts and Algorithms. Lecture Notes for Chapter 9. Introduction to Data Mining
Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004
More informationMachine Learning using MapReduce
Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous
More informationARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING)
ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING) Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Preliminaries Classification and Clustering Applications
More informationCOM CO P 5318 Da t Da a t Explora Explor t a ion and Analysis y Chapte Chapt r e 3
COMP 5318 Data Exploration and Analysis Chapter 3 What is data exploration? A preliminary exploration of the data to better understand its characteristics. Key motivations of data exploration include Helping
More informationMachine Learning and Data Mining. Clustering. (adapted from) Prof. Alexander Ihler
Machine Learning and Data Mining Clustering (adapted from) Prof. Alexander Ihler Unsupervised learning Supervised learning Predict target value ( y ) given features ( x ) Unsupervised learning Understand
More informationPERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA
PERFORMANCE ANALYSIS OF CLUSTERING ALGORITHMS IN DATA MINING IN WEKA Prakash Singh 1, Aarohi Surya 2 1 Department of Finance, IIM Lucknow, Lucknow, India 2 Department of Computer Science, LNMIIT, Jaipur,
More informationNeural Networks Lesson 5  Cluster Analysis
Neural Networks Lesson 5  Cluster Analysis Prof. Michele Scarpiniti INFOCOM Dpt.  Sapienza University of Rome http://ispac.ing.uniroma1.it/scarpiniti/index.htm michele.scarpiniti@uniroma1.it Rome, 29
More informationLecture 20: Clustering
Lecture 20: Clustering Wrapup of neural nets (from last lecture Introduction to unsupervised learning Kmeans clustering COMP424, Lecture 20  April 3, 2013 1 Unsupervised learning In supervised learning,
More informationData Exploration Data Visualization
Data Exploration Data Visualization What is data exploration? A preliminary exploration of the data to better understand its characteristics. Key motivations of data exploration include Helping to select
More informationClustering. 15381 Artificial Intelligence Henry Lin. Organizing data into clusters such that there is
Clustering 15381 Artificial Intelligence Henry Lin Modified from excellent slides of Eamonn Keogh, Ziv BarJoseph, and Andrew Moore What is Clustering? Organizing data into clusters such that there is
More informationAn Introduction to Cluster Analysis for Data Mining
An Introduction to Cluster Analysis for Data Mining 10/02/2000 11:42 AM 1. INTRODUCTION... 4 1.1. Scope of This Paper... 4 1.2. What Cluster Analysis Is... 4 1.3. What Cluster Analysis Is Not... 5 2. OVERVIEW...
More informationData Clustering. Dec 2nd, 2013 Kyrylo Bessonov
Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms kmeans Hierarchical Main
More informationSummary Data Mining & Process Mining (1BM46) Content. Made by S.P.T. Ariesen
Summary Data Mining & Process Mining (1BM46) Made by S.P.T. Ariesen Content Data Mining part... 2 Lecture 1... 2 Lecture 2:... 4 Lecture 3... 7 Lecture 4... 9 Process mining part... 13 Lecture 5... 13
More informationDistance based clustering
// Distance based clustering Chapter ² ² Clustering Clustering is the art of finding groups in data (Kaufman and Rousseeuw, 99). What is a cluster? Group of objects separated from other clusters Means
More informationData Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland
Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data
More informationAn Enhanced Clustering Algorithm to Analyze Spatial Data
International Journal of Engineering and Technical Research (IJETR) ISSN: 23210869, Volume2, Issue7, July 2014 An Enhanced Clustering Algorithm to Analyze Spatial Data Dr. Mahesh Kumar, Mr. Sachin Yadav
More informationData Exploration and Preprocessing. Data Mining and Text Mining (UIC 583 @ Politecnico di Milano)
Data Exploration and Preprocessing Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationClustering. Clustering. What is Clustering? What is Clustering? What is Clustering? Types of Data in Cluster Analysis
What is Clustering? Clustering Tpes of Data in Cluster Analsis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods What is Clustering? Clustering of data is
More information10810 /02710 Computational Genomics. Clustering expression data
10810 /02710 Computational Genomics Clustering expression data What is Clustering? Organizing data into clusters such that there is high intracluster similarity low intercluster similarity Informally,
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationL15: statistical clustering
Similarity measures Criterion functions Cluster validity Flat clustering algorithms kmeans ISODATA L15: statistical clustering Hierarchical clustering algorithms Divisive Agglomerative CSCE 666 Pattern
More informationClustering Hierarchical clustering and kmean clustering
Clustering Hierarchical clustering and kmean clustering Genome 373 Genomic Informatics Elhanan Borenstein The clustering problem: A quick review partition genes into distinct sets with high homogeneity
More informationComparison of Kmeans and Backpropagation Data Mining Algorithms
Comparison of Kmeans and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and
More informationStatistical Databases and Registers with some datamining
Unsupervised learning  Statistical Databases and Registers with some datamining a course in Survey Methodology and O cial Statistics Pages in the book: 501528 Department of Statistics Stockholm University
More informationChapter ML:XI (continued)
Chapter ML:XI (continued) XI. Cluster Analysis Data Mining Overview Cluster Analysis Basics Hierarchical Cluster Analysis Iterative Cluster Analysis DensityBased Cluster Analysis Cluster Evaluation Constrained
More informationHierarchical Cluster Analysis Some Basics and Algorithms
Hierarchical Cluster Analysis Some Basics and Algorithms Nethra Sambamoorthi CRMportals Inc., 11 Bartram Road, Englishtown, NJ 07726 (NOTE: Please use always the latest copy of the document. Click on this
More informationThere are a number of different methods that can be used to carry out a cluster analysis; these methods can be classified as follows:
Statistics: Rosie Cornish. 2007. 3.1 Cluster Analysis 1 Introduction This handout is designed to provide only a brief introduction to cluster analysis and how it is done. Books giving further details are
More informationCluster Analysis using R
Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other
More informationData visualization and clustering. Genomics is to no small extend a data science
Data visualization and clustering Genomics is to no small extend a data science [www.data2discovery.org] Data visualization and clustering Genomics is to no small extend a data science [Andersson et al.,
More informationData Mining KClustering Problem
Data Mining KClustering Problem Elham Karoussi Supervisor Associate Professor Noureddine Bouhmala This Master s Thesis is carried out as a part of the education at the University of Agder and is therefore
More informationClustering & Visualization
Chapter 5 Clustering & Visualization Clustering in highdimensional databases is an important problem and there are a number of different clustering paradigms which are applicable to highdimensional data.
More informationA comparison of various clustering methods and algorithms in data mining
Volume :2, Issue :5, 3236 May 2015 www.allsubjectjournal.com eissn: 23494182 pissn: 23495979 Impact Factor: 3.762 R.Tamilselvi B.Sivasakthi R.Kavitha Assistant Professor A comparison of various clustering
More informationRobotics 2 Clustering & EM. Giorgio Grisetti, Cyrill Stachniss, Kai Arras, Maren Bennewitz, Wolfram Burgard
Robotics 2 Clustering & EM Giorgio Grisetti, Cyrill Stachniss, Kai Arras, Maren Bennewitz, Wolfram Burgard 1 Clustering (1) Common technique for statistical data analysis to detect structure (machine learning,
More informationClustering and Data Mining in R
Clustering and Data Mining in R Workshop Supplement Thomas Girke December 10, 2011 Introduction Data Preprocessing Data Transformations Distance Methods Cluster Linkage Hierarchical Clustering Approaches
More informationData Mining. Practical Machine Learning Tools and Techniques. Classification, association, clustering, numeric prediction
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 2 of Data Mining by I. H. Witten and E. Frank Input: Concepts, instances, attributes Terminology What s a concept? Classification,
More informationPerformance Metrics for Graph Mining Tasks
Performance Metrics for Graph Mining Tasks 1 Outline Introduction to Performance Metrics Supervised Learning Performance Metrics Unsupervised Learning Performance Metrics Optimizing Metrics Statistical
More informationBIRCH: An Efficient Data Clustering Method For Very Large Databases
BIRCH: An Efficient Data Clustering Method For Very Large Databases Tian Zhang, Raghu Ramakrishnan, Miron Livny CPSC 504 Presenter: Discussion Leader: Sophia (Xueyao) Liang HelenJr, Birches. Online Image.
More informationStandardization and Its Effects on KMeans Clustering Algorithm
Research Journal of Applied Sciences, Engineering and Technology 6(7): 3993303, 03 ISSN: 0407459; eissn: 0407467 Maxwell Scientific Organization, 03 Submitted: January 3, 03 Accepted: February 5, 03
More informationPersonalized Hierarchical Clustering
Personalized Hierarchical Clustering Korinna Bade, Andreas Nürnberger Faculty of Computer Science, OttovonGuerickeUniversity Magdeburg, D39106 Magdeburg, Germany {kbade,nuernb}@iws.cs.unimagdeburg.de
More informationThey can be obtained in HQJHQH format directly from the home page at: http://www.engene.cnb.uam.es/downloads/kobayashi.dat
HQJHQH70 *XLGHG7RXU This document contains a Guided Tour through the HQJHQH platform and it was created for training purposes with respect to the system options and analysis possibilities. It is not intended
More informationA Study of Web Log Analysis Using Clustering Techniques
A Study of Web Log Analysis Using Clustering Techniques Hemanshu Rana 1, Mayank Patel 2 Assistant Professor, Dept of CSE, M.G Institute of Technical Education, Gujarat India 1 Assistant Professor, Dept
More informationSection 7.2 Linear Programming: The Graphical Method
Section 7.2 Linear Programming: The Graphical Method Man problems in business, science, and economics involve finding the optimal value of a function (for instance, the maimum value of the profit function
More informationDistances, Clustering, and Classification. Heatmaps
Distances, Clustering, and Classification Heatmaps 1 Distance Clustering organizes things that are close into groups What does it mean for two genes to be close? What does it mean for two samples to be
More informationClient Based Power Iteration Clustering Algorithm to Reduce Dimensionality in Big Data
Client Based Power Iteration Clustering Algorithm to Reduce Dimensionalit in Big Data Jaalatchum. D 1, Thambidurai. P 1, Department of CSE, PKIET, Karaikal, India Abstract  Clustering is a group of objects
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationData Mining and Visualization
Data Mining and Visualization Jeremy Walton NAG Ltd, Oxford Overview Data mining components Functionality Example application Quality control Visualization Use of 3D Example application Market research
More informationClustering in Machine Learning. By: Ibrar Hussain Student ID:
Clustering in Machine Learning By: Ibrar Hussain Student ID: 11021083 Presentation An Overview Introduction Definition Types of Learning Clustering in Machine Learning Kmeans Clustering Example of kmeans
More informationUnsupervised Data Mining (Clustering)
Unsupervised Data Mining (Clustering) Javier Béjar KEMLG December 01 Javier Béjar (KEMLG) Unsupervised Data Mining (Clustering) December 01 1 / 51 Introduction Clustering in KDD One of the main tasks in
More informationCluster Analysis: Basic Concepts and Methods
10 Cluster Analysis: Basic Concepts and Methods Imagine that you are the Director of Customer Relationships at AllElectronics, and you have five managers working for you. You would like to organize all
More informationUNSUPERVISED MACHINE LEARNING TECHNIQUES IN GENOMICS
UNSUPERVISED MACHINE LEARNING TECHNIQUES IN GENOMICS Dwijesh C. Mishra I.A.S.R.I., Library Avenue, New Delhi110 012 dcmishra@iasri.res.in What is Learning? "Learning denotes changes in a system that enable
More informationA Comparative Study of clustering algorithms Using weka tools
A Comparative Study of clustering algorithms Using weka tools Bharat Chaudhari 1, Manan Parikh 2 1,2 MECSE, KITRC KALOL ABSTRACT Data clustering is a process of putting similar data into groups. A clustering
More informationTerritorial Analysis for Ratemaking. Philip Begher, Dario Biasini, Filip Branitchev, David Graham, Erik McCracken, Rachel Rogers and Alex Takacs
Territorial Analysis for Ratemaking by Philip Begher, Dario Biasini, Filip Branitchev, David Graham, Erik McCracken, Rachel Rogers and Alex Takacs Department of Statistics and Applied Probability University
More informationSolving Quadratic Equations by Graphing. Consider an equation of the form. y ax 2 bx c a 0. In an equation of the form
SECTION 11.3 Solving Quadratic Equations b Graphing 11.3 OBJECTIVES 1. Find an ais of smmetr 2. Find a verte 3. Graph a parabola 4. Solve quadratic equations b graphing 5. Solve an application involving
More informationComparison and Analysis of Various Clustering Methods in Data mining On Education data set Using the weak tool
Comparison and Analysis of Various Clustering Metho in Data mining On Education data set Using the weak tool Abstract: Data mining is used to find the hidden information pattern and relationship between
More informationClustering Connectionist and Statistical Language Processing
Clustering Connectionist and Statistical Language Processing Frank Keller keller@coli.unisb.de Computerlinguistik Universität des Saarlandes Clustering p.1/21 Overview clustering vs. classification supervised
More informationCrossvalidation for detecting and preventing overfitting
Crossvalidation for detecting and preventing overfitting Note to other teachers and users of these slides. Andrew would be delighted if ou found this source material useful in giving our own lectures.
More informationClassification Techniques (1)
10 10 Overview Classification Techniques (1) Today Classification Problem Classification based on Regression Distancebased Classification (KNN) Net Lecture Decision Trees Classification using Rules Quality
More informationIris Sample Data Set. Basic Visualization Techniques: Charts, Graphs and Maps. Summary Statistics. Frequency and Mode
Iris Sample Data Set Basic Visualization Techniques: Charts, Graphs and Maps CS598 Information Visualization Spring 2010 Many of the exploratory data techniques are illustrated with the Iris Plant data
More informationIntroduction to Clustering
Introduction to Clustering Yumi Kondo Student Seminar LSK301 Sep 25, 2010 Yumi Kondo (University of British Columbia) Introduction to Clustering Sep 25, 2010 1 / 36 Microarray Example N=65 P=1756 Yumi
More informationSTATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and
Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Webbased Analytics Table
More informationNathan Poslusny,Shanshan Li Spring 2014 Instructor: Anita Wasilewska Stony Brook University. Cluster Analysis
Nathan Poslusny,Shanshan Li Spring 2014 Instructor: Anita Wasilewska Stony Brook University Cluster Analysis References 1) Han Jiawei and Kamber Micheline. Data Mining: Concepts and Techniques, 2 nd EdiQon.
More informationProtein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks YoungRae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics
More informationRobust Outlier Detection Technique in Data Mining: A Univariate Approach
Robust Outlier Detection Technique in Data Mining: A Univariate Approach Singh Vijendra and Pathak Shivani Faculty of Engineering and Technology Mody Institute of Technology and Science Lakshmangarh, Sikar,
More informationMedical Information Management & Mining. You Chen Jan,15, 2013 You.chen@vanderbilt.edu
Medical Information Management & Mining You Chen Jan,15, 2013 You.chen@vanderbilt.edu 1 Trees Building Materials Trees cannot be used to build a house directly. How can we transform trees to building materials?
More information