Recent advances in hybrid metaheuristics for dataclustering /

"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"--

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: De, Sourav, 1979- (Editor ), Dey, Sandip, 1977- (Editor ), Bhattacharyya, Siddhartha, 1975- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc., 2020.
Colección:The Wiley Series in Intelligent Signal and Data Processing
Temas:
Cluster analysis > Data processing.
Metaheuristics.
Classification automatique (Statistique) > Informatique.
Métaheuristiques.
Cluster analysis > Data processing
Metaheuristics
Acceso en línea:Texto completo

MARC

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082 0 4 |a 519.5/3  |2 23 
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245 0 0 |a Recent advances in hybrid metaheuristics for dataclustering /  |c edited by Sourav De, Sandip Dey, Siddhartha Bhattacharyya. 
264 1 |a Hoboken, NJ :  |b John Wiley & Sons, Inc.,  |c 2020. 
264 4 |c ©2020 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 0 |a The Wiley Series in Intelligent Signal and Data Processing 
504 |a Includes 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"--  |c Provided by publisher 
588 0 |a Online resource; title from digital title page (viewed on July 27, 2020). 
505 0 |a Cover -- 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 |a 1.9.2 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 |a 2.3.5 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 |a 2.8.1 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 |a 3.4 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 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Cluster analysis  |x Data processing. 
650 0 |a Metaheuristics. 
650 6 |a Classification automatique (Statistique)  |x Informatique. 
650 6 |a Métaheuristiques. 
650 7 |a Cluster analysis  |x Data processing  |2 fast 
650 7 |a Metaheuristics  |2 fast 
700 1 |a De, Sourav,  |d 1979-  |e editor. 
700 1 |a Dey, Sandip,  |d 1977-  |e editor. 
700 1 |a Bhattacharyya, Siddhartha,  |d 1975-  |e editor. 
758 |i has work:  |a Recent advances in hybrid metaheuristics for dataclustering (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCGYPfv4Xmcr77YPFC8HjqP  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |t Recent advances in hybrid metaheuristics for dataclustering.  |b First edition.  |d Hoboken, NJ : John Wiley & Sons, Inc., [2020]  |z 9781119551607  |w (DLC) 2020010571 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6219876  |z Texto completo 
938 |a Askews and Holts Library Services  |b ASKH  |n AH37048426 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6219876 
938 |a EBSCOhost  |b EBSC  |n 2493024 
938 |a YBP Library Services  |b YANK  |n 301319281 
994 |a 92  |b IZTAP 

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