Verfügbarkeit: Data clustering

Data clustering: algorithms and applications

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Format:	Buch
Sprache:	English
Veröffentlicht:	Boca Raton [u.a.] CRC Press 2014
Schriftenreihe:	Chapman & Hall, CRC data mining and knowledge discovery series
Schlagworte:	Data Mining Cluster-Analyse Algorithmus
Online-Zugang:	Inhaltsverzeichnis
Beschreibung:	Includes bibliographical references and index
Beschreibung:	XXVI, 622 S. Ill., graph. Darst. 26 cm
ISBN:	9781466558212

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Datensatz im Suchindex

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adam_text	Contents Prefaci xxj Kditor Biographies xx¡¡¡ Contributors xxv 1 An Introduction to Cluster Analysis 1 ( harn ( . Ліщатчіі 1.1 Introduction ..................................... 2 1.2 Common Techniques Used in (Muster Analysis .................. 3 1.2.1 Feature Selection Methods ......................... 4 1.2.2 Probabilistic and Generative Models ................... 4 1.2.3 Distance Based Algorithms ........................ 5 1.2.4 Density- and Grid-Based Methods ..................... 7 1.2.5 Leveraging Dimensionality Reduction Methods ............. 8 1.2.5.1 Generative Models lor Dimensionality Reduction ....... 8 1.2.5.2 Matrix Factorization and Co-Clustering ............ 8 1.2.5.3 Spectral Methods ........................ 10 1.2.6 Hie High Dimensional Scenario ...................... 11 1.2.7 Scalable Techniques Tor Cluster Analysis ................. 13 1.2.7.1 I/O Issues in Database Management .............. 13 1.2.7.2 Streaming Algorithms ..................... 14 1.2.7.3 lhe Big Data Framework .................... 14 I..Î Data Types Studied in Cluster Analysis ...................... 15 1.3.1 Clustering Categorical Data ........................ 15 1.3.2 Clustering Text Data ............................ 16 1.3.3 Clustering Multimedia Data ........................ 16 1.3.4 Clustering lime Series Data ........................ 17 1.3.5 Clustering Discrete Sequences ....................... 17 1.3.6 Clustering Network Data ......................... 18 1.3.7 Clustering Uncertain Data ......................... 19 14 Insights Gained from Different Variations of Cluster Analysis ........... 19 1.4.1 Visual Insights ............................... 20 1.4.2 Supervised Insights ............................ 20 1.4.3 Multiview and F.nsemble-Based Insights ................. 21 1.4.4 Validation-Based Insights ......................... 21 1.5 Discussion and Conclusions ............................ 22 vu VIH Contents 2 Feature Selection for Clustering: A Review Salem Alelyani, Jiliang Tang, and Huan Liu 2.1 Introduction 2.2 2.1.1 Data Clustering ........ 2.1.2 Feature Selection ........ 2. 1 .3 Feature Selection l or Clustering 2.1.3.1 Filter Model ..... 2.1.3.2 Wrapper Model . . . 2.1.3.3 Hybrid Model .... Feature Selection lor Clustering .... 2.2.1 Algorithms lor Generic Data 2.2. 2.2. 2.2. 2.2. 2.2. 2.2. Spectral Feature Selection (Sl FC) ............... .2 Laplacian Score (LS) ...................... .3 Feature Selection lor Sparse Clustering ............ .4 Localized Feature Selection Based on Scatter Separabili!) (LFSBSS) ............................ .5 Multicluster Feature Selection (MCFS) ............ .6 Feature Weighting ¿-Means................... 2.2.2 Algorithms lor Text Data ......................... 2.2.2.1 Term Frequency (TF) ...................... 2.2.2.2 Inverse Document Frequency (IDF) .............. 2.2.2.3 Term Frequency-Inverse Document Frequency (TF IDF) . . . 2.2.2.4 Chi Square Statistic ....................... 2.2.2.5 Frequent Term-Based Text Clustering ............. 2.2.2.6 Frequent Term Sequence .................... 2.2.3 Algorithms for Streaming Data ...................... 2.2.3.1 Text Stream Clustering Based on Adaptive Feature Selection (TSC-AFS) ........................... 2.2.3.2 High-Dimensional Projected Stream Clustering (HPStream) . 2.2.4 Algorithms for Linked Data ........................ 2.2.4.1 Challenges and Opportunities .................. 2.2.4.2 LUFS: An Unsupervised Feature Selection Framework for Linked Data ........................... 2.2.4.3 Conclusion and Future Work for Linked Data ......... 2.3 Discussions and Challenges ............................. 2.3.1 The Chicken or the Egg Dilemma ..................... 2.3.2 Model Selection: К and / ......................... 2.3.3 Scalability ................................. 2.3.4 Stability .................................. 3 Probabilistic Models for Clustering Hongbo Deng and Jiawei Han 3.1 Introduction ............. 3.2 Mixture Models ........... 3.2.1 Overview .......... 3.2.2 Gaussian Mixture Model . 3.2.3 Bernoulli Mixture Model . 3.2.4 Model Selection Criteria . 3.3 EM Algorithm and Its Variations 3.3.1 The General EM Algorithm 3.3.2 Mixture Models Revisited . 29 30 32 32 . . 34 35 35 35 Mi Mi Њ 37 38 39 40 41 41 42 42 42 44 45 47 47 48 50 50 51 52 53 53 54 54 55 61 61 62 62 64 67 68 69 69 73 Contents ¡x 3.3.3 Limitations of the HM Algorithm ..................... 75 3.3.4 Applications of the HM Algorithm .................... 76 3.4 Probabilistic Topic Models ............................. 76 3.4.1 Probabilistic Latent Semantic Analysis .................. 77 3.4.2 Latent Dirichlet Allocation ........................ 79 3.4.3 Variations and Hxtensions ......................... 81 3.5 Conclusions and Summary ............................. 81 4 A Survey of Fartitional and Hierarchical Clustering Algorithms 87 Chandan К. Reddy and Bhanukiran Vinzumuri 4.1 Introduction ..................................... 88 4.2 Parlitional Clustering Algorithms .......................... 89 4.2.1 A Means Clustering ............................ 89 4.2.2 Minimization of Sum of Squared Hrrors .................. 90 4.2.3 Factors Affecting A -Means ........................ 91 4.2.3.1 Popular Initialization Methods ................. 91 4.2.3.2 Hstimuting the Number of Clusters ............... 92 4.2.4 Variations of A -Means ........................... 93 4.2.4.1 A -Medoids Clustering ..................... 93 4.2.4.2 A -Medians Clustering ..................... 94 4.2.4.3 AT-Modes Clustering ...................... 94 4.2.4.4 Fuzzy A -Means Clustering ................... 95 4.2.4.5 Х -Means Clustering ....................... 95 4.2.4.6 Intelligent A -Means Clustering ................. 96 4.2.4.7 Bisecting A -Means Clustering ................. 97 4.2.4.8 Kernel A -Means Clustering ................... 97 4.2.4.9 Mean Shift Clustering ...................... 98 4.2.4.10 Weighted A -Means Clustering ................. 98 4.2.4.11 Genetic A -Means Clustering .................. 99 4.2.5 Making A -Means Faster .......................... 100 4.3 Hierarchical Clustering Algorithms ......................... 100 4.3.1 Agglomerative Clustering ......................... 101 4.3.1.1 Single and Complete Link ................... 101 4.3.1.2 Group Averaged and Centroid Agglomerative Clustering ... 102 4.3.1.3 Ward s Criterion ........................ 103 4.3.1.4 Agglomerative Hierarchical Clustering Algorithm ....... 103 4.3.1.5 Lance-Williams Dissimilarity Update Formula ........ 103 4.3.2 Divisive Clustering ............................. 104 4.3.2.1 Issues in Divisive Clustering .................. 104 4.3.2.2 Divisive Hierarchical Clustering Algorithm .......... 105 4.3.2.3 Minimum Spanning Tree-Based Clustering .......... 105 4.3.3 Other Hierarchical Clustering Algorithms ................. 106 4.4 Discussion and Summary .............................. Ю6 5 Density-Based Clustering Ш Martin Ester 5.1 Introduction ..................................... 5.2 DBSCAN ...................................... m 5.3 DHNCLUE ..................................... 115 5.4 OPTICS ....................................... П6 5.5 Other Algorithms .................................. и6 5.6 Subspace Clustering 5.7 Clustering Networks 5.8 Other Directions Contents ...... ПК ......................... ................................... 5.9 Conclusion ..................................... 127 6 Grid-Based Clustering Wei Cheng, Wei Wang, and Sandra Batista 6.1 Introduction ..................................... 6.2 The Classical Algorithms .............................. 6.2.1 Earliest Approaches: GRIDCLUS and BANG .............. Ш 6.2.2 STING and STlNCï+: ľhc Statistical Information ( ¡rid Approach .... U2 6.2.3 WaveCluster: Wavelets in Grid-Based Clustering ............. LU 6.3 Adaptive Grid-Based Algorithms .......................... ^ 6.3.1 AMR: Adaptive Mesh Refinement Clustering ............... 1·^ 6.4 Axis-Shifting Grid-Based Algorithms ....................... I Ui 6.4.1 NSGC: New Shifting Grid Clustering Algorithm ............. 1-М) 6.4.2 ADCC: Adaptable Deflect and Conquer Clustering ............ l^7 6.4.3 ASGC: Axis-Shifted Grid-Clustering ................... I-7 6.4.4 GD1LC: Grid-Based Densily-lsoLine Clustering Algorithm ....... 138 6.5 High-Dimensional Algorithms ........................... l-W 6.5.1 CLIQUE: The Classical High-Dimensional Algorithm .......... LW 6.5.2 Variants of CLIQUE ............................ 140 6.5.2.1 ENCLUS: Entropy-Based Approach .............. 140 6.5.2.2 MAFIA: Adaptive Grids in High Dimensions ......... 141 6.5.3 OptiGrid: Density-Based Optimal Grid Partitioning ........... 141 6.5.4 Variants of the OptiGrid Approach .................... 143 6.5.4.1 О -Cluster: A Scalable Approach ................ 143 6.5.4.2 CBF: Cell-Based Filtering ................... 144 6.6 Conclusions and Summary ............................. 145 7 Nonnegative Matrix Factorizations for Clustering: A Survey 149 Tao Li and Chris Ding 7.1 Introduction ..................................... 150 7.1.1 Background ................................ 150 7.1.2 NMF Formulations ............................. 151 7.2 NMF for Clustering: Theoretical Foundations ................... 151 7.2.1 NMF and Äľ-Means Clustering ....................... 151 7.2.2 NMF and Probabilistic Latent Semantic Indexing ............. 152 7.2.3 NMF and Kernel K-Means and Spectral Clustering ............ 152 7.2.4 NMF Boundedness Theorem ....................... 153 7.3 NMF Clustering Capabilities ............................ 153 7.3.1 Examples ....................... 153 7.3.2 Analysis .......... 15} 7.4 NMF Algorithms ................................. . 155 7.4.1 Introduction ....................... 1 55 7.4.2 Algorithm Development .......................... I55 7.4.3 Practical Issues in NMF Algorithms .................... 156 7.4.3.1 Initialization .................... 1^6 7.4.3.2 Stopping Criteria ........................ 156 7.4.3.3 Objective Function vs. Clustering Performance .. 157 7.4.3.4 Scalability ..................... 157 Contents x j 7.5 NMF Related Factorizations ........................... 158 7.6 NMF for Clustering: Hxtensions .......................... 161 7.6.1 Co-Clustering ............................... 161 7.6.2 Semisupervised Clustering ........................ 162 7.6.3 Semisupervised Co-Clustering ...................... 162 7.6.4 Consensus Clustering ........................... 163 7.6.5 (¡rapii Clustering .............................. 164 7.6.6 Other Clustering Hxtensions ........................ 164 7.7 Conclusions ..................................... 165 8 Spectral Clustering 177 J tal u Liu and Jiawei Han 8.1 Introduction ..................................... 177 8.2 Similarity (ìraph .................................. 179 8.3 Unnormali/.ed Spectral Clustering ......................... 180 8.3.1 Notation .................................. 180 8.3.2 Unnormalized Graph Laplacian ...................... 180 8.3.3 Spectrum Analysis ............................. 181 8.3.4 Unnormalized Spectral Clustering Algorithm ............... 182 8.4 Normalized Spectral Clustering ........................... 182 8.4.1 Normalized Graph Laplacian ....................... 183 8.4.2 Spectrum Analysis ............................. 184 8.4.3 Normalized Spectral Clustering Algorithm ................ 184 8.5 Graph Cut View ................................... 185 8.5.1 Ratio Cut Relaxation ............................ 186 8.5.2 Normalized Cut Relaxation ........................ 187 8.6 Random Walks View ................................ 188 8.7 Connection to Laplacian Eigenmap......................... 189 8.8 Connection to Kernel Ar-Means and Nonnegative Matrix Factorization ...... 191 8.9 Large Scale Spectral Clustering ........................... 192 8.10 Further Reading ................................... 194 9 Clustering High-Dimensional Data 201 Arthur Zimek 9.1 Introduction ..................................... 201 9.2 The Curse of Dimensionality ........................... 202 9.2.1 Different Aspects of the Curse ..................... 202 9.2.2 Consequences ............................... 206 9.3 Clustering Tasks in Subspaces of High-Dimensional Data ............. 206 9.3.1 Categories of Subspaces .......................... 206 9.3.1.1 Axis-Parallel Subspaces .................... 206 9.3.1.2 Arbitrarily Oriented Subspaces ................. 207 9.3.1.3 Special Cases .......................... 207 9.3.2 Search Spaces for the Clustering Problem ................. 207 9.4 Fundamental Algorithmic Ideas .......................... 208 9.4.1 Clustering in Axis-Parallel Subspaces ................... 208 9.4.1.1 Cluster Model .......................... 208 9.4.1.2 Basic Techniques ........................ 208 9.4.1.3 Clustering Algorithms ..................... 210 9.4.2 Clustering in Arbitrarily Oriented Subspaces ............... 215 9.4.2.1 Cluster Model .......................... 215 xii Contents 9.4.2.2 Basic Techniques and Kxample Algorithms .......... 216 9.5 Open Questions and Current Research Directions ................. - 9.6 Conclusion ..................................... 10 A Survey of Stream Clustering Algorithms Cham С Aggarwal ^ 10.1 Introduction ..................................... ~ 10.2 Methods Based on Partitioning Representatives .................. -■ · 10.2.1 The STREAM Algorithm ......................... 233 10.2.2 CluStrcam: The Microclustering Framework ............... 23~> 10.2.2.1 Microcluster Definition ..................... —Ъ 10.2.2.2 Pyramidal Time Frame ..................... 236 10.2.2.3 Online Clustering with CluStream ............... 237 10.3 Density-Based Stream Clustering .......................... 23(> 10.3.1 DenStream: Density-Based Microclustering ............... 240 10.3.2 Grid-Based Streaming Algorithms .................... 241 10.3.2.1 D-Stream Algorithm ...................... 241 10.3.2.2 Other Grid-Based Algorithms ................. 242 10.4 Probabilistic Streaming Algorithms ........................ 243 10.5 Clustering High-Dimensional Streams ....................... 243 10.5.1 The HPSTREAM Method ......................... 244 10.5.2 Other High-Dimensional Streaming Algorithms ............. 244 10.6 Clustering Discrete and Categorical Streams .................... 245 10.6.1 Clustering Binary Data Streams with ¿Means .............. 245 10.6.2 The StreamCluCD Algorithm ....................... 245 10.6.3 Massive-Domain Clustering ........................ 246 10.7 Text Stream Clustering ............................... 249 10.8 Other Scenarios for Stream Clustering ....................... 252 10.8.1 Clustering Uncertain Data Streams .................... 253 10.8.2 Clustering Graph Streams ......................... 253 10.8.3 Distributed Clustering of Data Streams .................. 254 10.9 Discussion and Conclusions ............................ 254 11 Big Data Clustering 259 Hanghang Tong and U Kang 11.1 Introduction ........................ 259 11.2 One-Pass Clustering Algorithms .......................... 260 11.2.1 CLARANS: Fighting with Exponential Search Space .......... 260 11.2.2 BIRCH: Fighting with Limited Memory ................. 261 11.2.3 CURE: Fighting with the Irregular Clusters ................ 263 11.3 Randomized Techniques for Clustering Algorithms ................ 263 11.3.1 Locality-Preserving Projection ........... 264 11.3.2 Global Projection ................ -^ 11.4 Parallel and Distributed Clustering Algorithms ........ . .......... 268 11.4.1 General Framework ............... 11.4.2 DBDC: Density-Based Clustering . . 11.4.3 ParMETIS: Graph Partitioning .......... . . . . . . . . . . . 269 11.4.4 PKMeans: Aľ-Means with MapReduce ........... .. . 270 11.4.5 DisCo: Co-Clustering with MapReduce ............ 271 11.4.6 BoW: Subspace Clustering with MapReduce ......... 11.5 Conclusion ........... Contents xjii 12 Clustering Categorical Data 277 Bill Andreopoulos 12.1 Introduction ................... - 278 12.2 Goals ol Categorical Clustering ........................... 279 12.2.1 Clustering Road Map ........................... 280 12.3 Similarity Measures l or Categorical Data ..................... 282 12.3.1 The Hamming Distance in Categorical and Binary Data ......... 282 12.3.2 Probabilistic Measures ........................... 283 12.3.3 Information-Theorelic Measures ..................... 283 12.3.4 Context-Based Similarity Measures .................... 284 12.4 Descriptions of Algorithms ............................. 284 12.4.1 Piirtition-Based Clustering ......................... 284 12.4.1.1 Л -Modes ............................. 284 12.4.1.2 Ar-Prototypes (Mixed Categorical and Numerical) ....... 285 12.4.1.3 l-uz/.y A-Modes ......................... 286 12.4.1.4 Squeezer ............................ 286 12.4.1.5 COOLCAT ........................... 286 12.4.2 Hierarchical Clustering .......................... 287 12.4.2.1 ROCK .............................. 287 12.4.2.2 COBWHB ............................ 288 12.4.2.3 LIMBO ............................. 289 12.4.3 Density-Based Clustering ......................... 289 12.4.3.1 Projected (Subspace) Clustering ................ 290 12.4.3.2 CACTUS ............................ 290 12.4.3.3 CLICKS ............................. 291 12.4.3.4 STIRR .............................. 291 12.4.3.5 CLOPE ............................. 292 12.4.3.6 HIHRDENC: Hierarchical Density-Based Clustering ..... 292 12.4.3.7 MULIC: Multiple Layer Incremental Clustering ........ 293 12.4.4 Model-Based Clustering .......................... 296 12.4.4.1 BILCOM Empirical Bayesian (Mixed Categorical and Numer¬ ical) ............................... 296 12.4.4.2 AutoClass (Mixed Categorical and Numerical) ........ 296 12.4.4.3 SV M Clustering (Mixed Categorical and Numerical) ..... 297 12.5 Conclusion ..................................... 298 13 Document Clustering: The Next Frontier 305 David C. Anas tasi и , Andrea Tagare l li, and George Kary pis 13.1 Introduction ..................................... 306 13.2 Modelinga Document ............................... 306 13.2.1 Preliminaries ................................ 306 13.2.2 The Vector Space Model .......................... 307 13.2.3 Alternate Document Models ........................ 309 13.2.4 Dimensionality Reduction for Text .................... 309 13.2.5 Characterizing Extremes .......................... 310 13.3 General Purpose Document Clustering ....................... 311 13.3.1 Similarity/Dissimilarity-Based Algorithms ................ 311 13.3.2 Density-Based Algorithms ......................... 312 13.3.3 Adjacency-Based Algorithms ....................... 313 13.3.4 Generative Algorithms ........................... 313 13.4 Clustering Long Documents ............................ 315 Contents xiv λ 1 5 13.4.1 Document Segmentation ....................... 13.4.2 Clustering Segmented Documents ...................... 13.4.3 Simultaneous Segment Identification and Clustering ............- 13.5 Clustering Short Documents ............................. 13.5.1 General Methods for Short Document Clustering .............. ł- 13.5.2 Clustering with Knowledge Infusion ....................-_ 13.5.3 Clustering Web Snippets ........................... ~ 13.5.4 Clustering Microblogs ............................- 13.6 Conclusion ...................................... ~ 11(1 14 Clustering Multimedia Data Shen-Fu Tsai, Guo-Jun Qi, Shiyu Chang, Min-Hsuan Tsui, and Thomas S. Hwms 14.1 Introduction ...................................... U0 14.2 Clustering with Image Data .............................. i4() 14.2.1 Visual Words Learning ............................ ^ 14.2.2 Face Clustering and Annotation ....................... u- 14.2.3 Photo Album Event Recognition ...................... ^ 14.2.4 Image Segmentation ............................. w 14.2.5 Large-Scale Image Classification ..................... ^45 14.3 Clustering with Video and Audio Data ....................... 347 14.3.1 Video Summarization ............................^48 14.3.2 Video Event Detection ........................... 349 14.3.3 Video Story Clustering ........................... 350 14.3.4 Music Summarization ........................... 350 14.4 Clustering with Multimodal Data.......................... 351 14.5 Summary and Future Directions .......................... 353 15 Time-Series Data Clustering 357 Dimitrios Kotsakos, Goce Trajcevski, Dimitrios Gunopulos, and Charu С. Aggarwal 15.1 Introduction ..................................... 358 15.2 The Diverse Formulations for Time-Series Clustering ............... 359 15.3 Online Correlation-Based Clustering ........................ 360 15.3.1 Selective Muscles and Related Methods .................. 361 15.3.2 Sensor Selection Algorithms for Correlation Clustering ......... 362 15.4 Similarity and Distance Measures ......................... 363 15.4.1 Univariate Distance Measures ....................... 363 15.4.1.1 Lp Distance ........................... З6З 15.4.1.2 Dynamic Time Warping Distance ............... 364 15.4.1.3 EDIT Distance ......................... M-,5 15.4.1.4 Longest Common Subsequence ................ 365 15.4.2 Multivariate Distance Measures ............... }66 15.4.2.1 Multidimensional Lp Distance ................. 366 15.4.2.2 Multidimensional DTW ............... ^67 15.4.2.3 Multidimensional LCSS .............. З68 15.4.2.4 Multidimensional Edit Distance ................ З68 15.4.2.5 Multidimensional Subsequence Matching . 368 15.5 Shape-Based Time-Series Clustering Techniques .......... ...... 369 15.5.1 ќ -Means Clustering .............. .......... 15.5.2 Hierarchical Clustering ........... ............ ^7] 15.5.3 Density-Based Clustering ...........]............. ηΊ Contents xv 15.5.4 Trajectory (Mustering ........................... 372 15.6 Time-Series Clustering Applications ........................ 374 15.7 Conclusions ..................... 375 16 Clustering Biological Data 381 Chandan К. Ready, Mohammad Al Hasan, and Mohammed J . Zaki 16.1 Introduction ..................................... 382 16.2 Clustering Microarray Data ............................. 383 16.2.1 Proximity Measures ............................ 383 16.2.2 Categorization of Algorithms ....................... 384 16.2.3 Standard (Mustering Algorithms ...................... 385 16.2.3.1 Hierarchical Clustering ..................... 385 16.2.3.2 Probabilistic (Mustering ..................... 386 16.2.3.3 (ìraph-Thcoretic Clustering ................... 386 16.2.3.4 Sell-Organizing Maps ...................... 387 16.2.3.5 Other (Mustering Methods ................... 387 16.2.4 Hicluslering ................................ 388 16.2.4.1 types and Structures of Biclusters ............... 389 16.2.4.2 Biclustering Algorithms .................... 390 16.2.4.3 Recent Developments ...................... 391 16.2.5 Triclustering ................................ 391 16.2.6 Time-Series (iene Expression Data Clustering .............. 392 16.2.7 Cluster Validation ............................. 393 16.3 Clustering Biological Networks .......................... 394 16.3.1 Characteristics of PPI Network Data ................... 394 16.3.2 Network (Mustering Algorithms ...................... 394 16.3.2.1 Molecular Complex Detection ................. 394 16.3.2.2 Markov (Mustering ....................... 395 16.3.2.3 Neighborhood Search Methods ................. 395 16.3.2.4 Clique Percolation Method ................... 395 16.3.2.5 Ensemble Clustering ...................... 396 16.3.2.6 Other (Mustering Methods ................... 396 16.3.3 Cluster Validation and Challenges ..................... 397 16.4 Biological Sequence (Mustering ........................... 397 16.4.1 Sequence Similarity Metrics ........................ 397 16.4.1.1 Alignment-Based Similarity .................. 398 16.4.1.2 Keyword-Based Similarity ................... 398 16.4.1.3 Kernel-Based Similarity .................... 399 16.4.1.4 Model-Based Similarity ..................... 399 16.4.2 Sequence (Mustering Algorithms ..................... 399 16.4.2.1 Subsequence-Based Clustering ................. 399 16.4.2.2 Graph-Based Clustering .................... 400 16.4.2.3 Probabilistic Models ...................... 402 16.4.2.4 Suffix Free and Suffix Array-Based Method .......... 403 16.5 Software Packages ................................. 403 16.6 Discussion and Summary .............................. 405 xvi Contents 415 17 Network Clustering Srinivasan Parthasarathy and S M Faisal 17.1 Introduction ..................................... 17.2 Background and Nomenclature ........................... 17.3 Problem Definition ................................. 17.4 Common Evaluation Criteria ............................ 17.5 Partitioning with Geometric Information ...................... 17.5.1 Coordinate Bisection ............................ 4\|l) 17.5.2 Inerţial Bisection .............................. 4^} 17.5.3 Geometric Partitioning ........................... 17.6 Graph Growing and Greedy Algorithms ...................... 421 17.6.1 Kernighan-Lin Algorithm ......................... ■-- 17.7 Agglomerativeand Divisive Clustering ....................... 4- 17.8 Spectral Clustering ................................. 424 17.8.1 Similarity Graphs ............................. 4-s 17.8.2 Types of Similarity Graphs ........................--> 17.8.3 Graph Laplacians ............................. 42ft 17.8.3.1 Unnormal ized Graph Laplacian ................ 426 17.8.3.2 Normalized Graph Laplacians ................. 427 17.8.4 Spectral Clustering Algorithms ...................... 427 17.9 Markov Clustering ................................. 428 17.9.1 Regularized MCL (RMCL): Improvement over MCI ........... 429 17.10 Multilevel Partitioning ............................... 430 17.11 Local Partitioning Algorithms ...........................432 17.12 Hypergraph Partitioning ..............................433 17.13 Emerging Methods for Partitioning Special Graphs ................435 17.13.1 Bipartite Graphs .............................. 435 17.13.2 Dynamic Graphs .............................. 436 17.13.3 Heterogeneous Networks ......................... 437 17.13.4 Directed Networks ............................. 438 17.13.5 Combining Content and Relationship Information ............ 439 17.13.6 Networks with Overlapping Communities ................ 440 17.13.7 Probabilistic Methods ................ 442 17.14 Conclusion ...................... 44 t 18 A Survey of Uncertain Data Clustering Algorithms 457 Charu C. Aggarwal 18.1 Introduction ................... ,^7 18.2 Mixture Model Clustering of Uncertain Data ........... 459 18.3 Density-Based Clustering Algorithms . ............. 46() 18.3.1 FDBSCAN Algorithm . .................... ш) 18.3.2 FOPTICS Algorithm ......... ................. 46, 18.4 Partitional Clustering Algorithms . ................. Д 18.4.1 The UK-Means Algorithm ....................... Jí 18.4.2 The CK-Means Algorithm .... . .................... 4^ 18.4.3 Clustering Uncertain Data with Voronoi Diagrams ............ 464 8.4.4 Approximation Algorithms for Clustering Uncertain Data ........ 464 18.4.5 Speeding Up Distance Computations ..... ,, s 18.5 Clustering Uncertain Data Streams ................ 18.5.1 The UMicro Algorithm ........................ 18.5.2 The LuMicro Algorithm ....................... ** Contents XVII 18.5.3 Enhancements to Stream Clustering .................... 471 18.6 Clustering Uncertain Data in High Dimensionality ................. 472 18.6.1 Subspace Clustering of Uncertain Data .................. 473 18.6.2 I iPStream: Projected Clustering of Uncertain Data Streams ....... 474 18.7 Clustering with the Possible Worlds Model .................... 477 18.8 Clustering Uncertain Graphs ...................... 47g 18.9 Conclusions and Summary .......................... 47g 19 Concepts of Visual and Interactive Clustering 483 A lexatuU r l І і une burg 19.1 Introduction ..................................... 4g3 19.2 Direct Visual and Interactive Clustering ...................... 4g4 19.2.1 Scatlerplots ................................. 4g5 19.2.2 Parallel Coordinates ............................ 488 19.2.3 Discussion ................................. 49I 19.3 Visual Interactive Steering of Clustering ...................... 491 19.3.1 Visual Assessment of Convergence of Clustering Algorithm ....... 491 19.3.2 Interactive Hierarchical Clustering .................... 492 19.3.3 Visual Clustering with SOMs ....................... 494 19.3.4 Discussion ................................. 494 19.4 Interactive Comparison and Combination of Clusterings .............. 495 19.4.1 Spacco! Clusterings ............................ 495 19.4.2 Visualization ................................ 497 19.4.3 Discussion ................................. 497 19..5 Visualization of Clusters for Sense-Making .................... 497 19.6 Summary ...................................... 500 20 Semisuperviscd Clustering 505 Amrudin Agavic and Arindam Banerjee 20.1 Introduction ..................................... 506 20.2 Clustering with Pointwise and Pairvvise Semisupervision ............. 507 20.2.1 Semisupervised Clustering Based on Seeding ............... 507 20.2.2 Semisupervised Clustering Based on Pairvvise Constraints ........ 508 20.2.3 Active Learning for Semisupervised Clustering .............. 511 20.2.4 Semisupervised Clustering Based on User Feedback ........... 512 20.2.5 Semisupervised Clustering Based on Nonnegative Matrix Factorization . 513 20.3 Semisupervised Graph Cuts ............................. 513 20.3.1 Semisupervised Unnormalized Cut .................... 515 20.3.2 Semisupervised Ratio Cut ......................... 515 20.3.3 Semisupervised Normalized Cut ...................... 516 20.4 A Unified View of Label Propagation ....................... 517 20.4.1 Generalized Label Propagation ...................... 517 20.4.2 Gaussian Fields .............................. 517 20.4.3 Tikhonov Regularization (T1KREG) ................... 518 20.4.4 Local and Global Consistency ....................... 518 20.4.5 Related Methods .............................. 5l9 20.4.5.1 Cluster Kernels ......................... 519 20.4.5.2 Gaussian Random Walks EM (GWEM) ............ 519 20.4.5.3 Linear Neighborhood Propagation ............... 520 20.4.6 Label Propagation and Green s Function ................. 521 20.4.7 Label Propagation and Semisupervised Graph Cuts ............ 521 Contents xviu 521 20.5 Semisupervised Embedding ......................... .Ί1 20.5. 1 Nonlinear Manifold Embedding ...................... .-- 20.5.2 Semisupervised Embedding ........................ ~[ 20.5.2. 1 Unconstrained Semisupervised Embedding .......... -■ 20.5.2.2 Constrained Seinisuperviscd Embedding ............ ^- 20.6 Comparative Experimental Analysis ........................ ^~ 20.6. 1 Experimental Results ........................... ]~ 20.6.2 Semisupervised Embedding Methods ................... ^- 20.7 Conclusions ....................................... 21 Alternative Clustering Analysis: A Review 535 James Bailey 21.1 Introduction ..................................... ^ -1 21.2 Technical Preliminaries ............................... ;i7 21.3 Multiple Clustering Analysis Using Alternative Clusterings ............ -VW 21.3.1 Alternative Clustering Algorithms: A Taxonomy ............. six 21.3.2 Unguided Generation ........................... -vW 21.3.2.1 Naive .............................. -w 21.3.2.2 Meta Clustering ......................... 5-w 21.3.2.3 Eigenvectors of the Laplacian Matrix .............. 540 21.3.2.4 Decorrelated ¿-Means and Convoiutional EM ......... 540 21.3.2.5 САМІ .............................. 540 21.3.3 Guided Generation with Constraints .................... 541 21.3.3.1 COALA ............................. 541 21.3.3.2 Constrained Optimization Approach .............. 541 21.3.3.3 MAXIMUS ........................... 542 21.3.4 Orthogonal Transformation Approaches ................. 543 21.3.4.1 Orthogonal Views ........................ 543 21.3.4.2 ADFT .............................. 543 21.3.5 Information Theoretic ........................... 544 21.3.5.1 Conditional Information Bottleneck (СІВ) ........... 544 21.3.5.2 Conditional Ensemble Clustering ................ 544 21.3.5.3 NACI .............................. 544 21.3.5.4 mSC ............................... 545 21.4 Connections to Multivievv Clustering and Subspace Clustering .......... 545 21.5 Future Research Issues .................. 547 21.6 Summary ...................... 547 22 Cluster Ensembles: Theory and Applications 551 Joydeep Ghosh and Ayan Acharya 22.1 Introduction .............. ^1 22.2 The Cluster Ensemble Problem .................. 554 22.3 Measuring Similarity Between Clustering Solutions ......... .555 22.4 Cluster Ensemble Algorithms ................. .......... 55Я 22.4.1 Probabilistic Approaches to Cluster Ensembles .............. 558 22.4.1.1 A Mixture Model for Cluster Ensembles (MMCE) ...... 558 22.4.1.2 Bayesian Cluster Ensembles (ВСЕ) .............. 558 22.4.1.3 Nonparametric Bayesian Cluster Ensembles (NPBCE) .... 559 22.4.2 Pairwise Similarity-Based Approaches .................. 560 22.4.2.1 Methods Based on Ensemble Co-Association Matrix ..... 560 Contents xix 22.4.2.2 Relating Consensus Clustering to Other Optimization Formu¬ lations ........................ 5^2 22.4.3 Direct Approaches I/sing Cluster Labels ................. 562 22.4.3.1 Graph Partitioning ....................... 562 22.4.3.2 Cumulative Voting ....................... 563 22.5 Applications of Consensus Clustering ....................... 564 22.5.1 (iene Expression Data Analysis ...................... 564 22.5.2 Image Segmentation ............................ 564 22.6 Concluding Remarks ................................ 566 23 Clustering Validation Measures 571 Hui Xiong and У.Іюпцтои Li 23.1 Introduction ..................................... 572 23.2 External Clustering Validation Measures ...................... 573 23.2.1 An Overview of External Clustering Validation Measures ........ 574 23.2.2 Defective Validation Measures ...................... 575 23.2.2.1 K-Means: The Uniform Effect ................. 575 23.2.2.2 A Necessary Selection Criterion ................ 576 23.2.2.3 The Cluster Validation Results ................. 576 23.2.2.4 The Issues with the Defective Measures ............ 577 23.2.2.5 Improving the Defective Measures ............... 577 23.2.3 Measure Normalization .......................... 577 23.2.3.1 Normalizing the Measures ................... 578 23.2.3.2 The DCV Criterion ....................... 581 23.2.3.3 The Effect of Normalization .................. 583 23.2.4 Measure Properties ............................. 584 23.2.4.1 The Consistency Between Measures .............. 584 23.2.4.2 Properties of Measures ..................... 586 23.2.4.3 Discussions ........................... 589 23.3 Internal Clustering Validation Measures ...................... 589 23.3.1 An Overview oflnternal Clustering Validation Measures ......... 589 23.3.2 Understanding of Internal Clustering Validation Measures ........ 592 23.3.2.1 The Impact of Monotonicity .................. 592 23.3.2.2 The Impact of Noise ...................... 593 23.3.2.3 The Impact of Density ..................... 594 23.3.2.4 The Impact of Subclusters ................... 595 23.3.2.5 The Impact of Skewed Distributions .............. 596 23.3.2.6 The Impact of Arbitrary Shapes ................ 598 23.3.3 Properties of Measures ........................... 600 23.4 Summary ...................................... 601 24 Educational and Software Resources for Data Clustering 607 Charu C. Aggarwal and Chandan K. Reddy 24.1 Introduction ..................................... 607 24.2 Educational Resources ............................... 24.2.1 Books on Data Clustering ......................... 608 24.2.2 Popular Survey Papers on Data Clustering ................ 608 24.3 Software for Data Clustering ............................ ^JO 24.3.1 Free and Open-Source Software ...................... 610 24.3.1.1 General Clustering Software .................. 610 243 1 2 Specialized Clustering Software ................ 610 xx Contents 24.3.2 Commercial Packages ........................... 611 24.3.3 Data Benchmarks for Software and Research ............... 611 24.4 Summary ...................................... 612 Index 617
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spelling	Data clustering algorithms and applications ed. by Charu C. Aggarwal ... Boca Raton [u.a.] CRC Press 2014 XXVI, 622 S. Ill., graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Chapman & Hall, CRC data mining and knowledge discovery series Includes bibliographical references and index Data Mining (DE-588)4428654-5 gnd rswk-swf Cluster-Analyse (DE-588)4070044-6 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Cluster-Analyse (DE-588)4070044-6 s Algorithmus (DE-588)4001183-5 s DE-604 Aggarwal, Charu C. 1970- Sonstige (DE-588)133500101 oth Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026184401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Data clustering algorithms and applications Data Mining (DE-588)4428654-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd Algorithmus (DE-588)4001183-5 gnd
subject_GND	(DE-588)4428654-5 (DE-588)4070044-6 (DE-588)4001183-5
title	Data clustering algorithms and applications
title_auth	Data clustering algorithms and applications
title_exact_search	Data clustering algorithms and applications
title_full	Data clustering algorithms and applications ed. by Charu C. Aggarwal ...
title_fullStr	Data clustering algorithms and applications ed. by Charu C. Aggarwal ...
title_full_unstemmed	Data clustering algorithms and applications ed. by Charu C. Aggarwal ...
title_short	Data clustering
title_sort	data clustering algorithms and applications
title_sub	algorithms and applications
topic	Data Mining (DE-588)4428654-5 gnd Cluster-Analyse (DE-588)4070044-6 gnd Algorithmus (DE-588)4001183-5 gnd
topic_facet	Data Mining Cluster-Analyse Algorithmus
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026184401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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