Data Clustering / / edited by Niansheng Tang.
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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Place / Publishing House: | London : : IntechOpen,, 2022. |
Year of Publication: | 2022 |
Language: | English |
Series: | IntechOpen book series. Artificial intelligence
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Physical Description: | 1 online resource (126 pages) |
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Data Clustering / edited by Niansheng Tang. London : IntechOpen, 2022. 1 online resource (126 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier IntechOpen book series. Artificial intelligence Description based on publisher supplied metadata and other sources. 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. 1. Introductory Chapter: Development of Data Clustering -- 2. Clustering Algorithms: An Exploratory Review -- 3. Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches -- Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data -- 5. Application of Jump Diffusion Models in Insurance Claim Estimation -- 6. Fuzzy Perceptron Learning for Non-Linearly Separable Patterns -- . Semantic Map: Bringing Together Groups and Discourses. Artificial intelligence. Cluster analysis. 1-83969-887-X Tang, Niansheng, editor. |
language |
English |
format |
eBook |
author2 |
Tang, Niansheng, |
author_facet |
Tang, Niansheng, |
author2_variant |
n t nt |
author2_role |
TeilnehmendeR |
title |
Data Clustering / |
spellingShingle |
Data Clustering / IntechOpen book series. Artificial intelligence 1. Introductory Chapter: Development of Data Clustering -- 2. Clustering Algorithms: An Exploratory Review -- 3. Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches -- Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data -- 5. Application of Jump Diffusion Models in Insurance Claim Estimation -- 6. Fuzzy Perceptron Learning for Non-Linearly Separable Patterns -- . Semantic Map: Bringing Together Groups and Discourses. |
title_full |
Data Clustering / edited by Niansheng Tang. |
title_fullStr |
Data Clustering / edited by Niansheng Tang. |
title_full_unstemmed |
Data Clustering / edited by Niansheng Tang. |
title_auth |
Data Clustering / |
title_new |
Data Clustering / |
title_sort |
data clustering / |
series |
IntechOpen book series. Artificial intelligence |
series2 |
IntechOpen book series. Artificial intelligence |
publisher |
IntechOpen, |
publishDate |
2022 |
physical |
1 online resource (126 pages) |
contents |
1. Introductory Chapter: Development of Data Clustering -- 2. Clustering Algorithms: An Exploratory Review -- 3. Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches -- Assessing Heterogeneity of Two-Part Model via Bayesian Model-Based Clustering with Its Application to Cocaine Use Data -- 5. Application of Jump Diffusion Models in Insurance Claim Estimation -- 6. Fuzzy Perceptron Learning for Non-Linearly Separable Patterns -- . Semantic Map: Bringing Together Groups and Discourses. |
isbn |
1-83969-888-8 1-83969-887-X |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q335 |
callnumber-sort |
Q 3335 D383 42022 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.3 |
dewey-sort |
16.3 |
dewey-raw |
006.3 |
dewey-search |
006.3 |
work_keys_str_mv |
AT tangniansheng dataclustering |
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Data Clustering / |
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