05875cam a2200493Ma 4500 ocn963702404 OCoLC 20190328114817.0 m o d cr |n||||||||| 161118s2016 xx ob 001 0 eng d IDEBK eng pn IDEBK N$T YDX EBLCP NLE OCLCO OPELS OCLCQ IDEBK OCLCF N$T UMI STF IDB UPM OCLCQ OTZ OCLCQ U3W MERUC REB D6H CEF KSU DEBBG AU@ OCLCQ LVT CNCGM 964298618 964545062 967513911 0128118415 (electronic bk.) 9780128118412 (electronic bk.) 0128116544 9780128116548 (OCoLC)963702404 (OCoLC)964298618 (OCoLC)964545062 (OCoLC)967513911 QA76.9.D343 COM 000000 bisacsh 006.312 23 Yang, Yun. Temporal Data Mining via Unsupervised Ensemble Learning / [electronic resource] Elsevier Science, 2016. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Includes bibliographical references and index. Front Cover -- Temporal Data Mining via Unsupervised Ensemble Learning -- Temporal Data Mining via Unsupervised Ensemble Learning -- Copyright -- Contents -- List of Figures -- List of Tables -- Acknowledgments -- 1 -- Introduction -- 1.1 BACKGROUND -- 1.2 PROBLEM STATEMENT -- 1.3 OBJECTIVE OF BOOK -- 1.4 OVERVIEW OF BOOK -- 2 -- Temporal Data Mining -- 2.1 INTRODUCTION -- 2.2 REPRESENTATIONS OF TEMPORAL DATA -- 2.2.1 TIME DOMAIN-BASED REPRESENTATIONS -- 2.2.2 TRANSFORMATION-BASED REPRESENTATIONS -- Piecewise Local Statistics -- Piecewise Discrete Wavelet Transforms -- Polynomial Curve Fitting -- Discrete Fourier Transforms -- 2.2.3 GENERATIVE MODEL-BASED REPRESENTATIONS -- 2.3 SIMILARITY MEASURES -- 2.3.1 SIMILARITY IN TIME -- 2.3.2 SIMILARITY IN SHAPE -- 2.3.3 SIMILARITY IN CHANGE -- 2.4 MINING TASKS -- 2.5 SUMMARY -- 3 -- Temporal Data Clustering -- 3.1 INTRODUCTION -- 3.2 OVERVIEW OF CLUSTERING ALGORITHMS -- 3.2.1 PARTITIONAL CLUSTERING -- K-means -- Hidden Markov Model-Based K-Models Clustering -- 3.2.2 HIERARCHICAL CLUSTERING -- Single Linkage -- Complete Linkage -- Average Linkage -- HMM-Based Agglomerative Clustering -- HMM-Based Divisive Clustering -- 3.2.3 DENSITY-BASED CLUSTERING -- Density-Based Spatial Clustering of Applications with Noise -- 3.2.4 MODEL-BASED CLUSTERING -- EM Algorithm -- HMM-Based Hybrid Partitional-Hierarchical Clustering -- HMM-Based Hierarchical Metaclustering -- 3.3 CLUSTERING VALIDATION -- 3.3.1 CLASSIFICATION ACCURACY -- 3.3.2 ADJUSTED RAND INDEX -- 3.3.3 JACCARD INDEX -- 3.3.4 MODIFIED HUBERT'S <U+0044> INDEX -- 3.3.5 DUNN'S VALIDITY INDEX -- 3.3.6 DAVIES-BOULDIN VALIDITY INDEX -- 3.3.7 NORMALIZED MUTUAL INFORMATION -- 3.4 SUMMARY -- 4 -- Ensemble Learning -- 4.1 INTRODUCTION -- 4.2 ENSEMBLE LEARNING ALGORITHMS -- Bagging -- Boosting -- 4.3 COMBINING METHODS -- Linear Combiner -- Product Combiner. Majority Voting Combiner -- 4.4 DIVERSITY OF ENSEMBLE LEARNING -- 4.5 CLUSTERING ENSEMBLE -- 4.5.1 CONSENSUS FUNCTIONS -- 4.5.1.1 Hypergraphic Partitioning Approach -- Cluster-Based Similarity Partitioning Algorithm -- Hypergraph-Partitioning Algorithm -- Meta-Clustering Algorithm -- 4.5.1.2 Coassociation-Based Approach -- 4.5.1.3 Voting-Based Approach -- 4.5.2 OBJECTIVE FUNCTION -- 4.6 SUMMARY -- 5 -- HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique -- 5.1 INTRODUCTION -- 5.2 HMM-BASED HYBRID META-CLUSTERING ENSEMBLE -- 5.2.1 MOTIVATION -- 5.2.2 MODEL DESCRIPTION -- 5.3 SIMULATION -- 5.3.1 HMM-GENERATED DATA SET -- 5.3.2 CBF DATA SET -- 5.3.3 TIME SERIES BENCHMARKS -- 5.3.4 MOTION TRAJECTORY -- 5.4 SUMMARY -- 6 -- Unsupervised Learning via an Iteratively Constructed Clustering Ensemble -- 6.1 INTRODUCTION -- 6.2 ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE -- 6.2.1 MOTIVATION -- 6.2.2 MODEL DESCRIPTION -- 6.3 SIMULATION -- 6.3.1 CYLINDER-BELL-FUNNEL DATA SET -- 6.3.2 TIME SERIES BENCHMARKS -- 6.3.3 MOTION TRAJECTORY -- 6.4 SUMMARY -- 7 -- Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations -- 7.1 INTRODUCTION -- 7.2 WEIGHTED CLUSTERING ENSEMBLE WITH DIFFERENT REPRESENTATIONS OF TEMPORAL DATA -- 7.2.1 MOTIVATION -- 7.2.2 MODEL DESCRIPTION -- 7.2.3 WEIGHTED CONSENSUS FUNCTION -- Partition Weighting Scheme -- Weighted Similarity Matrix -- Candidate Consensus Partition Generation -- 7.2.4 AGREEMENT FUNCTION -- 7.2.5 ALGORITHM ANALYSIS -- 7.3 SIMULATION -- 7.3.1 TIME SERIES BENCHMARKS -- 7.3.2 MOTION TRAJECTORY -- 7.3.3 TIME-SERIES DATA STREAM -- 7.4 SUMMARY -- 8 -- Conclusions, Future Work -- Appendix -- A.1 WEIGHTED CLUSTERING ENSEMBLE ALGORITHM ANALYSIS -- A.2 IMPLEMENTATION OF HMM-BASED META-CLUSTERING ENSEMBLE IN MATLAB CODE. A.3 IMPLEMENTATION OF ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE IN MATLAB CODE -- A.4 IMPLEMENTATION OF WCE WITH DIFFERENT REPRESENTATIONS -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- V -- W -- Back Cover. Data mining. Temporal databases. Machine learning. COMPUTERS General. bisacsh Data mining. fast (OCoLC)fst00887946 Machine learning. fast (OCoLC)fst01004795 Temporal databases. fast (OCoLC)fst01147471 Electronic books. Print version: Yang, Yun. Temporal Data Mining via Unsupervised Ensemble Learning. Elsevier Science, 2016 0128116544 9780128116548 (OCoLC)954534995 ScienceDirect http://www.sciencedirect.com/science/book/9780128116548 247469 247469