Data Clustering
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...
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| Autres auteurs: | |
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| Format: | Électronique Chapitre de livre |
| Langue: | anglais |
| Publié: |
IntechOpen
2022
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| Collection: | Artificial Intelligence
10 |
| Sujets: | |
| Accès en ligne: | DOAB: download the publication DOAB: description of the publication |
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| Résumé: | 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. |
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| Description matérielle: | 1 electronic resource (126 p.) |
| ISBN: | intechopen.95124 9781839698880 9781839698873 9781839698897 |
| Accès: | Open Access |