Clustering. Bruno Martins. 1 st Semester 2012/2013
|
|
- Cornelia Green
- 6 years ago
- Views:
Transcription
1 Departamento de Engenharia Informática Instituto Superior Técnico 1 st Semester 2012/2013 Slides baseados nos slides oficiais do livro Mining the Web c Soumen Chakrabarti.
2 Outline 1 Motivation Basic Concepts 2 Bottom-Up Top-Down 3 Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
3 Outline Motivation Basic Concepts 1 Motivation Basic Concepts 2 3
4 Motivation Motivation Basic Concepts Problem: Query word could be ambiguous: E.g., query star retrieves documents about astronomy, animals, etc. Solution: clustering document responses to queries along lines of different topics Problem: Manual construction of topic hierarchies and taxonomies Solution: preliminary clustering of large samples of web documents Problem: Speeding up similarity search Solution: restrict the search for documents similar to a query to most representative cluster(s)
5 An Example Motivation Basic Concepts
6 Motivation Basic Concepts Cluster Hypothesis: Given a suitable clustering of a collection, if the user is interested in document d (or term t), he is likely to be interested in other members of the cluster to which d (t) belongs Task: Evolve measures of similarity to cluster a collection of documents/terms into groups within which similarity within a cluster is larger than across clusters
7 Concepts Motivation Basic Concepts Two important paradigms: Bottom-up agglomerative clustering Top-down partitioning Visualization techniques: of corpus in a low-dimensional space Characterizing the entities: Internally: Vector space model, probabilistic models Externally: Measure of similarity/dissimilarity between pairs
8 Parameters Motivation Basic Concepts General parameters: Similarity measure: ρ(d 1,d 2 ) E.g., cosine similarity Distance measure: δ(d 1,d 2 ) E.g., Euclidean distance Number of clusters: k Problems: Large number of noisy dimensions Notion of noise is application dependent
9 Motivation Basic Concepts Bottom-up clustering Top-down clustering Self-organization map Multidimensional scaling Latent semantic indexing Generative models and probabilistic approaches Single topic per document Documents correspond to mixtures of multiple topics
10 Outline Bottom-Up Top-Down 1 2 Bottom-Up Top-Down 3
11 Bottom-Up Top-Down Partition document collection into k clusters [D 1,D 2,...,D k ] Choices: Minimize intra-cluster distance: i d 1,d 2 D i δ(d 1,d 2 ) Maximize intra-cluster semblance: i d 1,d 2 D i ρ(d 1,d 2 ) If cluster representations D i are available Minimize i d D i δ(d, D i ) Maximize i d D i ρ(d, D i ) Soft clustering d assigned to with D i confidence z d,i Find z d,i so as to minimize i d D i z d,i δ(d, D i ) or maximize i d D i z d,i ρ(d, D i ) Two ways to get partitions: bottom-up clustering and top-down clustering
12 Hierarchical Agglomerative Bottom-Up Top-Down Initially G is a collection of singleton groups, each with one document d Repeat Find Γ, G with max similarity measure, s(γ ) Merge group Γ with group Use above info to plot the hierarchical merging process (dendogram) To get desired number of clusters: cut across any level of the dendogram
13 Example Dendogram Bottom-Up Top-Down
14 Similarity Measures Bottom-Up Top-Down Self-Similarity Average pair wise similarity between documents in Φ s(φ) = 1 ) ( Φ 2 d 1,d 2 Φ s(d 1,d 2 ) s(d 1,d 2 ) is the inter-document similarity measure (e.g., cosine of tfidf vectors) Other criteria: Maximum/minimum pair wise similarity between documents in the clusters Complexity: O(n 2 logn) with n 2 space
15 Top-Down Bottom-Up Top-Down Uses an internal representation for documents as well as clusters Partitions documents into k clusters 2 variants Hard: 0/1 assignment of documents to clusters Soft: documents belong to clusters with fractional scores Termination When assignment of documents to clusters ceases to change much, or when cluster centroids move negligibly over successive iterations
16 The k-means Algorithm Bottom-Up Top-Down Hard k-means Choose k arbitrary centroids Assign each document to nearest centroid Recompute centroids Contribution for updating cluster centroid { η(d µc ) if µ c is closest to d µ c = d 0 otherwise µ c µ c + µ c The parameter η is called the learning rate
17 The k-means Algorithm Bottom-Up Top-Down Soft k-means Don t break close ties between document assignments to clusters and don t make documents contribute to a single cluster which wins narrowly The contribution for updating cluster centroid µ c from document d is related to the current similarity between both 1/ d µ c 2 µ c = η γ 1/ d µ γ 2(d µ c)
18 An Example Bottom-Up Top-Down (online demo)
19 Outline Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
20 Visualization Techniques Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Goal: of corpus in a low-dimensional space Hierarchical Agglomerative (HAC) lends itself easily to visualization Self-Organization Map (SOM) Technique related to k-means Multidimensional scaling (MDS) minimize the distortion of interpoint distances in the low-dimensional embedding as compared to the dissimilarity given in the input data Latent Semantic Indexing (LSI) Linear transformations to reduce number of dimensions
21 Self-Organizing Maps Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Like soft k-means Determine association between clusters and documents Associate a representative vector with each cluster and iteratively refine Unlike k-means Embed the clusters in a low-dimensional space right from the beginning Large number of clusters can be initialized even if eventually many are to remain devoid of documents Each cluster can be a slot in a square/hexagonal grid. The grid structure defines the neighborhood N(c) for each cluster c Also involves a proximity function h(γ, c) between clusters
22 Update Rule Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Data item d activates node (closest cluster) as well as the neighborhood nodes E.g., all nodes within n hops Update rule for node under the influence of d is: µ γ µ γ +ηh(γ,c d )(d µ γ )
23 Example Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
24 Multidimensional Scaling Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Goal: Distance preserving low dimensional embedding of documents (often called Principal Coordinate Analysis) Symmetric inter-document distances are given a priori or computed from internal representation Coarse-grained user feedback User provides similarity d i,j between documents i and j With increasing feedback, prior distances are overridden Objective : Minimize the stress of embedding i,j stress = ( dˆ i,j d i,j ) 2 i,j d2 i,j
25 Problems Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Stress not easy to optimize Iterative hill climbing 1 Points (documents) assigned random coordinates by external heuristic 2 Points (one at a time) moved by small distance in direction of locally decreasing stress For n documents Each takes O(n) time to be moved Total O(n 2 ) time per relaxation Faster approach: FastMap [Christos Faloutsos and Lin 1995]
26 Extended Similarity Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing In the vector space model different terms have different meanings car automobile However, car and automobile are related They are likely to co-occur often Documents having related words are related Useful for search and clustering Two basic approaches: Hand-made thesaurus (e.g., WordNet) Co-occurrence and associations
27 Latent Semantic Indexing Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Vector-space model distinct orthogonal direction for each terms Not all terms are orthogonal E.g., car and automobile We need a matrix with a lower rank than the traditional document-term matrix This matrix can be found through Singular Value Decomposition
28 Singular Value Decomposition Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
29 Querying Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing A query q is projected to the new space ˆq = D 1 r r UT r T q T Cosine similarity can now be applied to the projections Results are often better than tfidf retrieval SVD filters out noise and discovers semantic associations between terms
30 Example Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
31 Example Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing
32 Problems Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Computationally expensive Can run in minutes to hours on a 10 3 to 10 4 collection Current implementations need to store the whole input matrix in memory Not feasible for the Web Probabilistic Latent Semantic Indexing adds a sounder probabilistic model Based on a mixture decomposition derived from a latent class model
33 Self-Organizing Maps Multidimensional Scaling Latent Semantic Indexing Questions?
CSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Cluster Analysis: Basic Concepts and Methods Huan Sun, CSE@The Ohio State University 09/25/2017 Slides adapted from UIUC CS412, Fall 2017, by Prof. Jiawei Han 2 Chapter 10.
More informationClustering Results. Result List Example. Clustering Results. Information Retrieval
Information Retrieval INFO 4300 / CS 4300! Presenting Results Clustering Clustering Results! Result lists often contain documents related to different aspects of the query topic! Clustering is used to
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Cluster Analysis: Basic Concepts and Methods Huan Sun, CSE@The Ohio State University Slides adapted from UIUC CS412, Fall 2017, by Prof. Jiawei Han 2 Chapter 10. Cluster
More informationBruno Martins. 1 st Semester 2012/2013
Link Analysis Departamento de Engenharia Informática Instituto Superior Técnico 1 st Semester 2012/2013 Slides baseados nos slides oficiais do livro Mining the Web c Soumen Chakrabarti. Outline 1 2 3 4
More informationClustering. Informal goal. General types of clustering. Applications: Clustering in information search and analysis. Example applications in search
Informal goal Clustering Given set of objects and measure of similarity between them, group similar objects together What mean by similar? What is good grouping? Computation time / quality tradeoff 1 2
More informationBBS654 Data Mining. Pinar Duygulu. Slides are adapted from Nazli Ikizler
BBS654 Data Mining Pinar Duygulu Slides are adapted from Nazli Ikizler 1 Classification Classification systems: Supervised learning Make a rational prediction given evidence There are several methods for
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 4th, 2014 Wolf-Tilo Balke and José Pinto Institut für Informationssysteme Technische Universität Braunschweig The Cluster
More informationCS490W. Text Clustering. Luo Si. Department of Computer Science Purdue University
CS490W Text Clustering Luo Si Department of Computer Science Purdue University [Borrows slides from Chris Manning, Ray Mooney and Soumen Chakrabarti] Clustering Document clustering Motivations Document
More informationInformation Retrieval and Organisation
Information Retrieval and Organisation Chapter 16 Flat Clustering Dell Zhang Birkbeck, University of London What Is Text Clustering? Text Clustering = Grouping a set of documents into classes of similar
More informationMultiDimensional Signal Processing Master Degree in Ingegneria delle Telecomunicazioni A.A
MultiDimensional Signal Processing Master Degree in Ingegneria delle Telecomunicazioni A.A. 205-206 Pietro Guccione, PhD DEI - DIPARTIMENTO DI INGEGNERIA ELETTRICA E DELL INFORMAZIONE POLITECNICO DI BARI
More informationCluster Analysis. Ying Shen, SSE, Tongji University
Cluster Analysis Ying Shen, SSE, Tongji University Cluster analysis Cluster analysis groups data objects based only on the attributes in the data. The main objective is that The objects within a group
More informationCS 1675 Introduction to Machine Learning Lecture 18. Clustering. Clustering. Groups together similar instances in the data sample
CS 1675 Introduction to Machine Learning Lecture 18 Clustering Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square Clustering Groups together similar instances in the data sample Basic clustering problem:
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 4/18/004 1
More informationDATA MINING LECTURE 7. Hierarchical Clustering, DBSCAN The EM Algorithm
DATA MINING LECTURE 7 Hierarchical Clustering, DBSCAN The EM Algorithm CLUSTERING What is a Clustering? In general a grouping of objects such that the objects in a group (cluster) are similar (or related)
More informationhttp://www.xkcd.com/233/ Text Clustering David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/lecture17-clustering.ppt Administrative 2 nd status reports Paper review
More informationCluster analysis. Agnieszka Nowak - Brzezinska
Cluster analysis Agnieszka Nowak - Brzezinska Outline of lecture What is cluster analysis? Clustering algorithms Measures of Cluster Validity What is Cluster Analysis? Finding groups of objects such that
More informationClustering CE-324: Modern Information Retrieval Sharif University of Technology
Clustering CE-324: Modern Information Retrieval Sharif University of Technology M. Soleymani Fall 2014 Most slides have been adapted from: Profs. Manning, Nayak & Raghavan (CS-276, Stanford) Ch. 16 What
More informationData Mining Cluster Analysis: Basic Concepts and Algorithms. Slides From Lecture Notes for Chapter 8. Introduction to Data Mining
Data Mining Cluster Analysis: Basic Concepts and Algorithms Slides From Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar Tan,Steinbach, Kumar Introduction to Data Mining
More informationClassification. 1 o Semestre 2007/2008
Classification Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2007/2008 Slides baseados nos slides oficiais do livro Mining the Web c Soumen Chakrabarti. Outline 1 2 3 Single-Class
More informationLesson 3. Prof. Enza Messina
Lesson 3 Prof. Enza Messina Clustering techniques are generally classified into these classes: PARTITIONING ALGORITHMS Directly divides data points into some prespecified number of clusters without a hierarchical
More informationClustering: Overview and K-means algorithm
Clustering: Overview and K-means algorithm Informal goal Given set of objects and measure of similarity between them, group similar objects together K-Means illustrations thanks to 2006 student Martin
More informationInformation Retrieval and Web Search Engines
Information Retrieval and Web Search Engines Lecture 7: Document Clustering May 25, 2011 Wolf-Tilo Balke and Joachim Selke Institut für Informationssysteme Technische Universität Braunschweig Homework
More informationCS7267 MACHINE LEARNING
S7267 MAHINE LEARNING HIERARHIAL LUSTERING Ref: hengkai Li, Department of omputer Science and Engineering, University of Texas at Arlington (Slides courtesy of Vipin Kumar) Mingon Kang, Ph.D. omputer Science,
More informationDimension reduction : PCA and Clustering
Dimension reduction : PCA and Clustering By Hanne Jarmer Slides by Christopher Workman Center for Biological Sequence Analysis DTU The DNA Array Analysis Pipeline Array design Probe design Question Experimental
More informationClustering Part 4 DBSCAN
Clustering Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville DBSCAN DBSCAN is a density based clustering algorithm Density = number of
More informationClustering Lecture 3: Hierarchical Methods
Clustering Lecture 3: Hierarchical Methods Jing Gao SUNY Buffalo 1 Outline Basics Motivation, definition, evaluation Methods Partitional Hierarchical Density-based Mixture model Spectral methods Advanced
More informationData Mining. Clustering. Hamid Beigy. Sharif University of Technology. Fall 1394
Data Mining Clustering Hamid Beigy Sharif University of Technology Fall 1394 Hamid Beigy (Sharif University of Technology) Data Mining Fall 1394 1 / 31 Table of contents 1 Introduction 2 Data matrix and
More informationCS 2750 Machine Learning. Lecture 19. Clustering. CS 2750 Machine Learning. Clustering. Groups together similar instances in the data sample
Lecture 9 Clustering Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square Clustering Groups together similar instances in the data sample Basic clustering problem: distribute data into k different groups
More informationClustering Algorithms for general similarity measures
Types of general clustering methods Clustering Algorithms for general similarity measures general similarity measure: specified by object X object similarity matrix 1 constructive algorithms agglomerative
More informationUniversity of Florida CISE department Gator Engineering. Clustering Part 4
Clustering Part 4 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville DBSCAN DBSCAN is a density based clustering algorithm Density = number of
More informationUnsupervised Learning
Outline Unsupervised Learning Basic concepts K-means algorithm Representation of clusters Hierarchical clustering Distance functions Which clustering algorithm to use? NN Supervised learning vs. unsupervised
More informationCS47300: Web Information Search and Management
CS47300: Web Information Search and Management Text Clustering Prof. Chris Clifton 19 October 2018 Borrows slides from Chris Manning, Ray Mooney and Soumen Chakrabarti Document clustering Motivations Document
More informationUnsupervised Learning
Networks for Pattern Recognition, 2014 Networks for Single Linkage K-Means Soft DBSCAN PCA Networks for Kohonen Maps Linear Vector Quantization Networks for Problems/Approaches in Machine Learning Supervised
More informationClustering CS 550: Machine Learning
Clustering CS 550: Machine Learning This slide set mainly uses the slides given in the following links: http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf
More informationCSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16)
CSE 7/5337: Information Retrieval and Web Search Document clustering I (IIR 16) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Schütze Institute for
More informationData Informatics. Seon Ho Kim, Ph.D.
Data Informatics Seon Ho Kim, Ph.D. seonkim@usc.edu Clustering Overview Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measurements,
More informationClustering. CE-717: Machine Learning Sharif University of Technology Spring Soleymani
Clustering CE-717: Machine Learning Sharif University of Technology Spring 2016 Soleymani Outline Clustering Definition Clustering main approaches Partitional (flat) Hierarchical Clustering validation
More information10701 Machine Learning. Clustering
171 Machine Learning Clustering What is Clustering? Organizing data into clusters such that there is high intra-cluster similarity low inter-cluster similarity Informally, finding natural groupings among
More informationText Analytics (Text Mining)
CSE 6242 / CX 4242 Apr 1, 2014 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer,
More informationUniversity of Florida CISE department Gator Engineering. Clustering Part 2
Clustering Part 2 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Partitional Clustering Original Points A Partitional Clustering Hierarchical
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 6: Flat Clustering Wiltrud Kessler & Hinrich Schütze Institute for Natural Language Processing, University of Stuttgart 0-- / 83
More informationKeywords Clustering, Goals of clustering, clustering techniques, clustering algorithms.
Volume 3, Issue 5, May 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Survey of Clustering
More information5/15/16. Computational Methods for Data Analysis. Massimo Poesio UNSUPERVISED LEARNING. Clustering. Unsupervised learning introduction
Computational Methods for Data Analysis Massimo Poesio UNSUPERVISED LEARNING Clustering Unsupervised learning introduction 1 Supervised learning Training set: Unsupervised learning Training set: 2 Clustering
More informationHierarchical Clustering
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 0 0 0 00
More informationMachine Learning for OR & FE
Machine Learning for OR & FE Unsupervised Learning: Clustering Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com (Some material
More informationHierarchical Clustering
Hierarchical Clustering 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
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval Mohsen Kamyar چهارمین کارگاه ساالنه آزمایشگاه فناوری و وب بهمن ماه 1391 Outline Outline in classic categorization Information vs. Data Retrieval IR Models Evaluation
More informationClustering algorithms
Clustering algorithms Machine Learning Hamid Beigy Sharif University of Technology Fall 1393 Hamid Beigy (Sharif University of Technology) Clustering algorithms Fall 1393 1 / 22 Table of contents 1 Supervised
More informationClustering: Overview and K-means algorithm
Clustering: Overview and K-means algorithm Informal goal Given set of objects and measure of similarity between them, group similar objects together K-Means illustrations thanks to 2006 student Martin
More informationIntroduction to Information Retrieval
Introduction to Information Retrieval http://informationretrieval.org IIR 16: Flat Clustering Hinrich Schütze Institute for Natural Language Processing, Universität Stuttgart 2009.06.16 1/ 64 Overview
More informationSearch Engines. Information Retrieval in Practice
Search Engines Information Retrieval in Practice All slides Addison Wesley, 2008 Classification and Clustering Classification and clustering are classical pattern recognition / machine learning problems
More informationBehavioral Data Mining. Lecture 18 Clustering
Behavioral Data Mining Lecture 18 Clustering Outline Why? Cluster quality K-means Spectral clustering Generative Models Rationale Given a set {X i } for i = 1,,n, a clustering is a partition of the X i
More informationData Mining Concepts & Techniques
Data Mining Concepts & Techniques Lecture No 08 Cluster Analysis Naeem Ahmed Email: naeemmahoto@gmailcom Department of Software Engineering Mehran Univeristy of Engineering and Technology Jamshoro Outline
More informationStatistics 202: Data Mining. c Jonathan Taylor. Week 8 Based in part on slides from textbook, slides of Susan Holmes. December 2, / 1
Week 8 Based in part on slides from textbook, slides of Susan Holmes December 2, 2012 1 / 1 Part I Clustering 2 / 1 Clustering Clustering Goal: Finding groups of objects such that the objects in a group
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Erik Velldal & Stephan Oepen Language Technology Group (LTG) September 23, 2015 Agenda Last week Supervised vs unsupervised learning.
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationINF4820. Clustering. Erik Velldal. Nov. 17, University of Oslo. Erik Velldal INF / 22
INF4820 Clustering Erik Velldal University of Oslo Nov. 17, 2009 Erik Velldal INF4820 1 / 22 Topics for Today More on unsupervised machine learning for data-driven categorization: clustering. The task
More informationBased on Raymond J. Mooney s slides
Instance Based Learning Based on Raymond J. Mooney s slides University of Texas at Austin 1 Example 2 Instance-Based Learning Unlike other learning algorithms, does not involve construction of an explicit
More informationMinoru SASAKI and Kenji KITA. Department of Information Science & Intelligent Systems. Faculty of Engineering, Tokushima University
Information Retrieval System Using Concept Projection Based on PDDP algorithm Minoru SASAKI and Kenji KITA Department of Information Science & Intelligent Systems Faculty of Engineering, Tokushima University
More informationCommunity Detection. Community
Community Detection Community In social sciences: Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group a.k.a. group,
More informationClustering: K-means and Kernel K-means
Clustering: K-means and Kernel K-means Piyush Rai Machine Learning (CS771A) Aug 31, 2016 Machine Learning (CS771A) Clustering: K-means and Kernel K-means 1 Clustering Usually an unsupervised learning problem
More informationTypes of general clustering methods. Clustering Algorithms for general similarity measures. Similarity between clusters
Types of general clustering methods Clustering Algorithms for general similarity measures agglomerative versus divisive algorithms agglomerative = bottom-up build up clusters from single objects divisive
More informationCS Introduction to Data Mining Instructor: Abdullah Mueen
CS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen LECTURE 8: ADVANCED CLUSTERING (FUZZY AND CO -CLUSTERING) Review: Basic Cluster Analysis Methods (Chap. 10) Cluster Analysis: Basic Concepts
More informationUnsupervised Data Mining: Clustering. Izabela Moise, Evangelos Pournaras, Dirk Helbing
Unsupervised Data Mining: Clustering Izabela Moise, Evangelos Pournaras, Dirk Helbing Izabela Moise, Evangelos Pournaras, Dirk Helbing 1 1. Supervised Data Mining Classification Regression Outlier detection
More informationCSE 347/447: DATA MINING
CSE 347/447: DATA MINING Lecture 6: Clustering II W. Teal Lehigh University CSE 347/447, Fall 2016 Hierarchical Clustering Definition Produces a set of nested clusters organized as a hierarchical tree
More informationText Analytics (Text Mining)
CSE 6242 / CX 4242 Text Analytics (Text Mining) Concepts and Algorithms Duen Horng (Polo) Chau Georgia Tech Some lectures are partly based on materials by Professors Guy Lebanon, Jeffrey Heer, John Stasko,
More informationBig Data Analytics! Special Topics for Computer Science CSE CSE Feb 9
Big Data Analytics! Special Topics for Computer Science CSE 4095-001 CSE 5095-005! Feb 9 Fei Wang Associate Professor Department of Computer Science and Engineering fei_wang@uconn.edu Clustering I What
More informationUnsupervised Learning. Presenter: Anil Sharma, PhD Scholar, IIIT-Delhi
Unsupervised Learning Presenter: Anil Sharma, PhD Scholar, IIIT-Delhi Content Motivation Introduction Applications Types of clustering Clustering criterion functions Distance functions Normalization Which
More informationIntroduction to Clustering
Introduction to Clustering Ref: Chengkai Li, Department of Computer Science and Engineering, University of Texas at Arlington (Slides courtesy of Vipin Kumar) What is Cluster Analysis? Finding groups of
More informationMachine Learning. Unsupervised Learning. Manfred Huber
Machine Learning Unsupervised Learning Manfred Huber 2015 1 Unsupervised Learning In supervised learning the training data provides desired target output for learning In unsupervised learning the training
More informationUnsupervised Learning : Clustering
Unsupervised Learning : Clustering Things to be Addressed Traditional Learning Models. Cluster Analysis K-means Clustering Algorithm Drawbacks of traditional clustering algorithms. Clustering as a complex
More informationModern Multidimensional Scaling
Ingwer Borg Patrick J.F. Groenen Modern Multidimensional Scaling Theory and Applications Second Edition With 176 Illustrations ~ Springer Preface vii I Fundamentals of MDS 1 1 The Four Purposes of Multidimensional
More informationHierarchical Clustering Lecture 9
Hierarchical Clustering Lecture 9 Marina Santini Acknowledgements Slides borrowed and adapted from: Data Mining by I. H. Witten, E. Frank and M. A. Hall 1 Lecture 9: Required Reading Witten et al. (2011:
More informationFlat Clustering. Slides are mostly from Hinrich Schütze. March 27, 2017
Flat Clustering Slides are mostly from Hinrich Schütze March 7, 07 / 79 Overview Recap Clustering: Introduction 3 Clustering in IR 4 K-means 5 Evaluation 6 How many clusters? / 79 Outline Recap Clustering:
More informationAssociation Rule Mining and Clustering
Association Rule Mining and Clustering Lecture Outline: Classification vs. Association Rule Mining vs. Clustering Association Rule Mining Clustering Types of Clusters Clustering Algorithms Hierarchical:
More information4. Ad-hoc I: Hierarchical clustering
4. Ad-hoc I: Hierarchical clustering Hierarchical versus Flat Flat methods generate a single partition into k clusters. The number k of clusters has to be determined by the user ahead of time. Hierarchical
More informationECLT 5810 Clustering
ECLT 5810 Clustering What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping
More informationUnsupervised Learning and Data Mining
Unsupervised Learning and Data Mining Unsupervised Learning and Data Mining Clustering Supervised Learning ó Decision trees ó Artificial neural nets ó K-nearest neighbor ó Support vectors ó Linear regression
More informationPart I. Hierarchical clustering. Hierarchical Clustering. Hierarchical clustering. Produces a set of nested clusters organized as a
Week 9 Based in part on slides from textbook, slides of Susan Holmes Part I December 2, 2012 Hierarchical Clustering 1 / 1 Produces a set of nested clusters organized as a Hierarchical hierarchical clustering
More informationClustering (COSC 488) Nazli Goharian. Document Clustering.
Clustering (COSC 488) Nazli Goharian nazli@ir.cs.georgetown.edu 1 Document Clustering. Cluster Hypothesis : By clustering, documents relevant to the same topics tend to be grouped together. C. J. van Rijsbergen,
More informationPV211: Introduction to Information Retrieval https://www.fi.muni.cz/~sojka/pv211
PV: Introduction to Information Retrieval https://www.fi.muni.cz/~sojka/pv IIR 6: Flat Clustering Handout version Petr Sojka, Hinrich Schütze et al. Faculty of Informatics, Masaryk University, Brno Center
More informationBig Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2017)
Big Data Infrastructure CS 489/698 Big Data Infrastructure (Winter 2017) Week 9: Data Mining (4/4) March 9, 2017 Jimmy Lin David R. Cheriton School of Computer Science University of Waterloo These slides
More informationGeneral Instructions. Questions
CS246: Mining Massive Data Sets Winter 2018 Problem Set 2 Due 11:59pm February 8, 2018 Only one late period is allowed for this homework (11:59pm 2/13). General Instructions Submission instructions: These
More informationCSE 6242 A / CS 4803 DVA. Feb 12, Dimension Reduction. Guest Lecturer: Jaegul Choo
CSE 6242 A / CS 4803 DVA Feb 12, 2013 Dimension Reduction Guest Lecturer: Jaegul Choo CSE 6242 A / CS 4803 DVA Feb 12, 2013 Dimension Reduction Guest Lecturer: Jaegul Choo Data is Too Big To Do Something..
More informationMethods for Intelligent Systems
Methods for Intelligent Systems Lecture Notes on Clustering (II) Davide Eynard eynard@elet.polimi.it Department of Electronics and Information Politecnico di Milano Davide Eynard - Lecture Notes on Clustering
More informationGene Clustering & Classification
BINF, Introduction to Computational Biology Gene Clustering & Classification Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Introduction to Gene Clustering
More informationHierarchical clustering
Hierarchical clustering Based in part on slides from textbook, slides of Susan Holmes December 2, 2012 1 / 1 Description Produces a set of nested clusters organized as a hierarchical tree. Can be visualized
More informationINF4820, Algorithms for AI and NLP: Evaluating Classifiers Clustering
INF4820, Algorithms for AI and NLP: Evaluating Classifiers Clustering Erik Velldal University of Oslo Sept. 18, 2012 Topics for today 2 Classification Recap Evaluating classifiers Accuracy, precision,
More informationClustering (COSC 416) Nazli Goharian. Document Clustering.
Clustering (COSC 416) Nazli Goharian nazli@cs.georgetown.edu 1 Document Clustering. Cluster Hypothesis : By clustering, documents relevant to the same topics tend to be grouped together. C. J. van Rijsbergen,
More informationMining Web Data. Lijun Zhang
Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems
More informationClustering in Data Mining
Clustering in Data Mining Classification Vs Clustering When the distribution is based on a single parameter and that parameter is known for each object, it is called classification. E.g. Children, young,
More informationToday s topic CS347. Results list clustering example. Why cluster documents. Clustering documents. Lecture 8 May 7, 2001 Prabhakar Raghavan
Today s topic CS347 Clustering documents Lecture 8 May 7, 2001 Prabhakar Raghavan Why cluster documents Given a corpus, partition it into groups of related docs Recursively, can induce a tree of topics
More informationINF4820 Algorithms for AI and NLP. Evaluating Classifiers Clustering
INF4820 Algorithms for AI and NLP Evaluating Classifiers Clustering Murhaf Fares & Stephan Oepen Language Technology Group (LTG) September 27, 2017 Today 2 Recap Evaluation of classifiers Unsupervised
More informationClustering and Dimensionality Reduction. Stony Brook University CSE545, Fall 2017
Clustering and Dimensionality Reduction Stony Brook University CSE545, Fall 2017 Goal: Generalize to new data Model New Data? Original Data Does the model accurately reflect new data? Supervised vs. Unsupervised
More informationMining Web Data. Lijun Zhang
Mining Web Data Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Introduction Web Crawling and Resource Discovery Search Engine Indexing and Query Processing Ranking Algorithms Recommender Systems
More informationDD2475 Information Retrieval Lecture 10: Clustering. Document Clustering. Recap: Classification. Today
Sec.14.1! Recap: Classification DD2475 Information Retrieval Lecture 10: Clustering Hedvig Kjellström hedvig@kth.se www.csc.kth.se/dd2475 Data points have labels Classification task: Finding good separators
More informationLecture Notes for Chapter 7. Introduction to Data Mining, 2 nd Edition. by Tan, Steinbach, Karpatne, Kumar
Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining, 2 nd Edition by Tan, Steinbach, Karpatne, Kumar Hierarchical Clustering Produces a set
More informationCHAPTER 4: CLUSTER ANALYSIS
CHAPTER 4: CLUSTER ANALYSIS WHAT IS CLUSTER ANALYSIS? A cluster is a collection of data-objects similar to one another within the same group & dissimilar to the objects in other groups. Cluster analysis
More informationClustering Part 3. Hierarchical Clustering
Clustering Part Dr Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville Hierarchical Clustering Two main types: Agglomerative Start with the points
More informationA Comparison of Document Clustering Techniques
A Comparison of Document Clustering Techniques M. Steinbach, G. Karypis, V. Kumar Present by Leo Chen Feb-01 Leo Chen 1 Road Map Background & Motivation (2) Basic (6) Vector Space Model Cluster Quality
More information