Data Clustering

ISBN 10: 183969887X / ISBN 13: 9781839698873

Editore: Intechopen Aug 2022, 2022

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This item is printed on demand - it takes 3-4 days longer - Neuware -In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data. 126 pp. Englisch.

Codice articolo 9781839698873

Dati bibliografici

• Titolo: Data Clustering
• Editore: Intechopen Aug 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 183969887X
• ISBN 13: 9781839698873
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 266 L x 185 A x 14 P (millimetri) 266 millimetri di larghezza per 185 millimetri di altezza per 14 millimetri di profondità
• Peso: 494 grammi

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Riassunto

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.