Recent Advances in Hybrid Metaheuristics for Data Clustering

187 Pages·2020·28.327 MB·English

by  Sourav De (editor), Sandip Dey (editor), Siddhartha Bhattacharyya (editor)

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RecentAdvancesinHybridMetaheuristicsforDataClustering
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Recent Advances in Hybrid Metaheuristics for
Data Clustering
Edited by
Sourav De
CoochBeharGovernmentEngineeringCollege,WestBengal,India
Sandip Dey
SukantaMahavidyalaya,WestBengal,India
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Siddhartha Bhattacharyya
CHRIST(DeemedtobeUniversity),Bangalore,India
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Thiseditionfirstpublished2020
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LibraryofCongressCataloging-in-PublicationData
Names:De,Sourav,1979-editor.|Dey,Sandip,1977-editor.|
Bhattacharyya,Siddhartha,1975-editor.
Title:Recentadvancesinhybridmetaheuristicsfordataclustering/edited
byDr.SouravDe,Dr.SandipDey,Dr.SiddharthaBhattacharyya.
Description:Firstedition.|Hoboken,NJ:JohnWiley&Sons,Inc.,[2020]
|Includesbibliographicalreferencesandindex.
Identifiers:LCCN2020010571(print)|LCCN2020010572(ebook)|ISBN
9781119551591(cloth)|ISBN9781119551614(adobepdf)|ISBN
9781119551607(epub)
Subjects:LCSH:Clusteranalysis–Dataprocessing.|Metaheuristics.
Classification:LCCQA278.55.R432020(print)|LCCQA278.55(ebook)|
DDC519.5/3–dc23
LCrecordavailableathttps://lccn.loc.gov/2020010571
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Dr.SouravDededicatesthisbooktohisrespectedparents,SatyaNarayanDeandTapasiDe;
hislovingwife,DebolinaGhosh;hisbelovedson,AishikDe;hissister,SoumiDe,andhis
in-laws.
Dr.SandipDeydedicatesthisbooktothelovingmemoryofhisfather,thelateDhananjoy
Dey;hisbelovedmother,Smt.GitaDey;hiswife,SwagataDeySarkar;hischildren,Sunishka
andShriaan;hissiblings,Kakali,Tanusree,andSanjoy;andhisnephews,Shreyashand
Adrishaan.
Dr.SiddharthaBhattacharyyadedicatesthisbooktohislatefather,AjitKumar
Bhattacharyya;hislatemother,HashiBhattacharyya;hisbelovedwife,Rashni,andhis
in-laws,AsisMukherjeeandPolyMukherjee.
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vii
Contents
ListofContributors xiii
SeriesPreface xv
Preface xvii
1 MetaheuristicAlgorithmsinFuzzyClustering 1
SouravDe,SandipDey,andSiddharthaBhattacharyya
1.1 Introduction 1
1.2 FuzzyClustering 1
1.2.1 Fuzzyc-means(FCM)clustering 2
(cid:2) 1.3 Algorithm 2 (cid:2)
1.3.1 SelectionofClusterCenters 3
1.4 GeneticAlgorithm 3
1.5 ParticleSwarmOptimization 5
1.6 AntColonyOptimization 6
1.7 ArtificialBeeColonyAlgorithm 7
1.8 LocalSearch-BasedMetaheuristicClusteringAlgorithms 7
1.9 Population-BasedMetaheuristicClusteringAlgorithms 8
1.9.1 GA-BasedFuzzyClustering 8
1.9.2 PSO-BasedFuzzyClustering 9
1.9.3 AntColonyOptimization–BasedFuzzyClustering 10
1.9.4 ArtificialBeeColonyOptimization–BasedFuzzyClustering 10
1.9.5 DifferentialEvolution–BasedFuzzyClustering 11
1.9.6 FireflyAlgorithm–BasedFuzzyClustering 12
1.10 Conclusion 13
References 13
2 HybridHarmonySearchAlgorithmtoSolvetheFeatureSelectionfor
DataMiningApplications 19
LaithMohammadAbualigah,MoflehAl-diabat,MohammadAlShinwan,
KhaldoonDhou,BisanAlsalibi,EssamSaidHanandeh,andMohammadShehab
2.1 Introduction 19
2.2 ResearchFramework 21
2.3 TextPreprocessing 22
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viii Contents
2.3.1 Tokenization 22
2.3.2 StopWordsRemoval 22
2.3.3 Stemming 23
2.3.4 TextDocumentRepresentation 23
2.3.5 TermWeight(TF-IDF) 23
2.4 TextFeatureSelection 24
2.4.1 MathematicalModeloftheFeatureSelectionProblem 24
2.4.2 SolutionRepresentation 24
2.4.3 FitnessFunction 24
2.5 HarmonySearchAlgorithm 25
2.5.1 ParametersInitialization 25
2.5.2 HarmonyMemoryInitialization 26
2.5.3 GeneratingaNewSolution 26
2.5.4 UpdateHarmonyMemory 27
2.5.5 ChecktheStoppingCriterion 27
2.6 TextClustering 27
2.6.1 MathematicalModeloftheTextClustering 27
2.6.2 FindClustersCentroid 27
2.6.3 SimilarityMeasure 28
2.7 k-meanstextclusteringalgorithm 28
2.8 ExperimentalResults 29
(cid:2) 2.8.1 EvaluationMeasures 29 (cid:2)
2.8.1.1 F-measureBasedonClusteringEvaluation 30
2.8.1.2 AccuracyBasedonClusteringEvaluation 31
2.8.2 ResultsandDiscussions 31
2.9 Conclusion 34
References 34
3 AdaptivePosition–BasedCrossoverintheGeneticAlgorithmforData
Clustering 39
ArnabGainandPrasenjitDey
3.1 Introduction 39
3.2 Preliminaries 40
3.2.1 Clustering 40
3.2.1.1 k-meansClustering 40
3.2.2 GeneticAlgorithm 41
3.3 RelatedWorks 42
3.3.1 GA-BasedDataClusteringbyBinaryEncoding 42
3.3.2 GA-BasedDataClusteringbyRealEncoding 43
3.3.3 GA-BasedDataClusteringforImbalancedDatasets 44
3.4 ProposedModel 44
3.5 Experimentation 46
3.5.1 ExperimentalSettings 46
3.5.2 DBIndex 47
3.5.3 ExperimentalResults 49
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Contents ix
3.6 Conclusion 51
References 57
4 ApplicationofMachineLearningintheSocialNetwork 61
BelfinR.V.,E.GraceMaryKanaga,andSumanKundu
4.1 Introduction 61
4.1.1 SocialMedia 61
4.1.2 BigData 62
4.1.3 MachineLearning 62
4.1.4 NaturalLanguageProcessing(NLP) 63
4.1.5 SocialNetworkAnalysis 64
4.2 ApplicationofClassificationModelsinSocialNetworks 64
4.2.1 SpamContentDetection 65
4.2.2 TopicModelingandLabeling 65
4.2.3 HumanBehaviorAnalysis 67
4.2.4 SentimentAnalysis 68
4.3 ApplicationofClusteringModelsinSocialNetworks 68
4.3.1 RecommenderSystems 69
4.3.2 SentimentAnalysis 70
4.3.3 InformationSpreadingorPromotion 70
4.3.4 Geolocation-SpecificApplications 70
(cid:2) 4.4 ApplicationofRegressionModelsinSocialNetworks 71 (cid:2)
4.4.1 SocialNetworkandHumanBehavior 71
4.4.2 EmotionContagionthroughSocialNetworks 73
4.4.3 RecommenderSystemsinSocialNetworks 74
4.5 ApplicationofEvolutionaryComputingandDeepLearninginSocial
Networks 74
4.5.1 EvolutionaryComputingandSocialNetwork 75
4.5.2 DeepLearningandSocialNetworks 75
4.6 Summary 76
Acknowledgments 77
References 78
5 PredictingStudents’GradesUsingCART,ID3,andMulticlassSVM
OptimizedbytheGeneticAlgorithm(GA):ACaseStudy 85
DebanjanKonar,RuchitaPradhan,TaniaDey,TejaswiniSapkota,
andPrativaRai
5.1 Introduction 85
5.2 LiteratureReview 87
5.3 DecisionTreeAlgorithms:ID3andCART 88
5.4 MulticlassSupportVectorMachines(SVMs)OptimizedbytheGenetic
Algorithm(GA) 90
5.4.1 GeneticAlgorithmsforSVMModelSelection 92
5.5 PreparationofDatasets 93
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x Contents
5.6 ExperimentalResultsandDiscussions 95
5.7 Conclusion 96
References 96
6 ClusterAnalysisofHealthCareDataUsingHybridNature-Inspired
Algorithms 101
KauserAhmedP,RishabhAgrawal
6.1 Introduction 101
6.2 RelatedWork 102
6.2.1 FireflyAlgorithm 102
6.2.2 k-meansAlgorithm 103
6.3 ProposedMethodology 104
6.4 ResultsandDiscussion 106
6.5 Conclusion 110
References 111
7 PerformanceAnalysisThroughaMetaheuristicKnowledge
Engine 113
InduChhabraandGunmalaSuri
7.1 Introduction 113
7.2 DataMiningandMetaheuristics 114
(cid:2) 7.3 ProblemDescription 115 (cid:2)
7.4 AssociationRuleLearning 116
7.4.1 AssociationMiningIssues 116
7.4.2 ResearchInitiativesandProjects 116
7.5 LiteratureReview 117
7.6 Methodology 119
7.6.1 Phase1:PatternSearch 120
7.6.2 Phase2:RuleMining 120
7.6.3 Phase3:KnowledgeDerivation 121
7.7 Implementation 121
7.7.1 TestIssues 121
7.7.2 SystemEvaluation 121
7.7.2.1 IndicatorMatrixFormulation 122
7.7.2.2 Phase1:FrequentPatternDerivation 123
7.7.2.3 Phase2:AssociationRuleFraming 123
7.7.2.4 Phase3:KnowledgeDiscoveryThroughMetaheuristicImplementation 123
7.8 PerformanceAnalysis 124
7.9 ResearchContributionsandFutureWork 125
7.10 Conclusion 126
References 126
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Contents xi
8 MagneticResonanceImageSegmentationUsingaQuantum-Inspired
ModifiedGeneticAlgorithm(QIANA)BasedonFRCM 129
SunandaDas,SouravDe,SandipDey,andSiddharthaBhattacharyya
8.1 Introduction 129
8.2 LiteratureSurvey 131
8.3 QuantumComputing 133
8.3.1 Quoit-QuantumBit 133
8.3.2 Entanglement 133
8.3.3 Measurement 133
8.3.4 QuantumGate 134
8.4 SomeQualityEvaluationIndicesforImageSegmentation 134
8.4.1 F(I) 134
8.4.2 F’(I) 135
8.4.3 Q(I) 135
8.5 Quantum-InspiredModifiedGeneticAlgorithm(QIANA)–BasedFRCM 135
8.5.1 Quantum-InspiredMEGA(QIANA)–BasedFRCM 136
8.6 ExperimentalResultsandDiscussion 139
8.7 Conclusion 147
References 147
9 AHybridApproachUsingthek-meansandGeneticAlgorithmsfor
(cid:2) ImageColorQuantization 151 (cid:2)
MarcosRobertoeSouza,AndersonCarlosSousaeSantos,andHelioPedrini
9.1 Introduction 151
9.2 Background 152
9.3 ColorQuantizationMethodology 154
9.3.1 CrossoverOperators 157
9.3.2 MutationOperators 158
9.3.3 FitnessFunction 158
9.4 ResultsandDiscussions 159
9.5 ConclusionsandFutureWork 168
Acknowledgments 168
References 168
Index 173
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