000			05661cam a22006618i 4500
001			on1152457624
003			OCoLC
005			20220711203602.0
006			m o d
007			cr |||||||||||
008			200228s2020 nju ob 001 0 eng
010			_a 2020010572
040			_aDLC _beng _erda _cDLC _dOCLCQ _dYDX _dOCLCF _dEBLCP _dDG1 _dUKAHL _dOCLCO
019			_a1157076982
020			_a9781119551607 _q(epub)
020			_a1119551609
020			_a9781119551614 _q(adobe pdf)
020			_a1119551617
020			_z9781119551591 _q(cloth)
020			_a9781119551621 _q(electronic bk. : oBook)
020			_a1119551625 _q(electronic bk. : oBook)
035			_a(OCoLC)1152457624 _z(OCoLC)1157076982
042			_apcc
050	0	0	_aQA278.55
082	0	0	_a519.5/3 _223
049			_aMAIN
245	0	0	_aRecent advances in hybrid metaheuristics for dataclustering / _cedited by Dr. Sourav De, Dr. Sandip Dey, Dr. Siddhartha Bhattacharyya.
250			_aFirst edition.
263			_a2006
264		1	_aHoboken, NJ : _bJohn Wiley & Sons, Inc., _c[2020]
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
490	1		_aThe Wiley Series in Intelligent Signal and Data Processing
504			_aIncludes bibliographical references and index.
520			_a"The book will elaborate on the fundamentals of different meta-heuristics and their application to data clustering. As a result, it will pave the way for designing and developing hybrid meta-heuristics to be applied to data clustering"-- _cProvided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
505	0		_aCover -- Title Page -- Copyright -- Contents -- List of Contributors -- Series Preface -- Preface -- Chapter 1 Metaheuristic Algorithms in Fuzzy Clustering -- 1.1 Introduction -- 1.2 Fuzzy Clustering -- 1.2.1 Fuzzy c-means (FCM) clustering -- 1.3 Algorithm -- 1.3.1 Selection of Cluster Centers -- 1.4 Genetic Algorithm -- 1.5 Particle Swarm Optimization -- 1.6 Ant Colony Optimization -- 1.7 Artificial Bee Colony Algorithm -- 1.8 Local Search-Based Metaheuristic Clustering Algorithms -- 1.9 Population-Based Metaheuristic Clustering Algorithms -- 1.9.1 GA-Based Fuzzy Clustering
505	8		_a1.9.2 PSO-Based Fuzzy Clustering -- 1.9.3 Ant Colony Optimization-Based Fuzzy Clustering -- 1.9.4 Artificial Bee Colony Optimization-Based Fuzzy Clustering -- 1.9.5 Differential Evolution-Based Fuzzy Clustering -- 1.9.6 Firefly Algorithm-Based Fuzzy Clustering -- 1.10 Conclusion -- References -- Chapter 2 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications -- 2.1 Introduction -- 2.2 Research Framework -- 2.3 Text Preprocessing -- 2.3.1 Tokenization -- 2.3.2 Stop Words Removal -- 2.3.3 Stemming -- 2.3.4 Text Document Representation
505	8		_a2.3.5 Term Weight (TF-IDF) -- 2.4 Text Feature Selection -- 2.4.1 Mathematical Model of the Feature Selection Problem -- 2.4.2 Solution Representation -- 2.4.3 Fitness Function -- 2.5 Harmony Search Algorithm -- 2.5.1 Parameters Initialization -- 2.5.2 Harmony Memory Initialization -- 2.5.3 Generating a New Solution -- 2.5.4 Update Harmony Memory -- 2.5.5 Check the Stopping Criterion -- 2.6 Text Clustering -- 2.6.1 Mathematical Model of the Text Clustering -- 2.6.2 Find Clusters Centroid -- 2.6.3 Similarity Measure -- 2.7 k-means text clustering algorithm -- 2.8 Experimental Results
505	8		_a2.8.1 Evaluation Measures -- 2.8.1.1 F-measure Based on Clustering Evaluation -- 2.8.1.2 Accuracy Based on Clustering Evaluation -- 2.8.2 Results and Discussions -- 2.9 Conclusion -- References -- Chapter 3 Adaptive Position-Based Crossover in the Genetic Algorithm for Data Clustering -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Clustering -- 3.2.1.1 k-means Clustering -- 3.2.2 Genetic Algorithm -- 3.3 Related Works -- 3.3.1 GA-Based Data Clustering by Binary Encoding -- 3.3.2 GA-Based Data Clustering by Real Encoding -- 3.3.3 GA-Based Data Clustering for Imbalanced Datasets
505	8		_a3.4 Proposed Model -- 3.5 Experimentation -- 3.5.1 Experimental Settings -- 3.5.2 DB Index -- 3.5.3 Experimental Results -- 3.6 Conclusion -- References -- Chapter 4 Application of Machine Learning in the Social Network -- 4.1 Introduction -- 4.1.1 Social Media -- 4.1.2 Big Data -- 4.1.3 Machine Learning -- 4.1.4 Natural Language Processing (NLP) -- 4.1.5 Social Network Analysis -- 4.2 Application of Classification Models in Social Networks -- 4.2.1 Spam Content Detection -- 4.2.2 Topic Modeling and Labeling -- 4.2.3 Human Behavior Analysis -- 4.2.4 Sentiment Analysis
590			_aJohn Wiley and Sons _bWiley Frontlist Obook All English 2020
650		0	_aCluster analysis _xData processing. _99040
650		0	_aMetaheuristics. _94799
650		7	_aCluster analysis _xData processing. _2fast _0(OCoLC)fst00864978 _99040
650		7	_aMetaheuristics. _2fast _0(OCoLC)fst02000551 _94799
655		4	_aElectronic books. _93294
700	1		_aDe, Sourav, _d1979- _eeditor. _99041
700	1		_aDey, Sandip, _d1977- _eeditor. _98090
700	1		_aBhattacharyya, Siddhartha, _d1975- _eeditor. _98092
776	0	8	_iPrint version: _tRecent advances in hybrid metaheuristics for dataclustering _bFirst edition. _dHoboken, NJ : John Wiley & Sons, Inc., [2020] _z9781119551591 _w(DLC) 2020010571
830		0	_aWiley Series in Intelligent Signal and Data Processing. _99042
856	4	0	_uhttps://doi.org/10.1002/9781119551621 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69270 _d69270