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...
保存先:
| フォーマット: | Online |
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| 言語: | 英語 |
| 出版事項: |
IntechOpen
2023
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| 主題: | |
| オンライン･アクセス: | ONIX_20230215_9781839698880_78 |
| タグ: |
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| _version_ | 1869517469510533120 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-97038 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-970382024-04-14T10:27:48Z Data Clustering Tang, Niansheng Databases thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining 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. 2023-02-15T14:36:46Z 2023-02-15T14:36:46Z 2022 book ONIX_20230215_9781839698880_78 2633-1403 9781839698880 9781839698873 9781839698897 https://directory.doabooks.org/handle/20.500.12854/97038 eng Artificial Intelligence image/jpeg n/a https://www.intechopen.com/books/10820 https://mts.intechopen.com/storage/books/10820/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.95124 10.5772/intechopen.95124 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781839698880 9781839698873 9781839698897 IntechOpen 10 126 open access |
| spellingShingle | Databases thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining Data Clustering |
| title | Data Clustering |
| title_full | Data Clustering |
| title_fullStr | Data Clustering |
| title_full_unstemmed | Data Clustering |
| title_short | Data Clustering |
| title_sort | data clustering |
| topic | Databases thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| topic_facet | Databases thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining |
| url | ONIX_20230215_9781839698880_78 |