LEADER 02568nam 2200421 450 001 9910633977103321 005 20231027112707.0 010 $a1-83969-888-8 024 7 $a10.5772/intechopen.95124 035 $a(CKB)5700000000338589 035 $a(NjHacI)995700000000338589 035 $a(EXLCZ)995700000000338589 100 $a20230330d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData Clustering /$fedited by Niansheng Tang 210 1$aLondon :$cIntechOpen,$d2022. 215 $a1 online resource (126 pages) 225 1 $aArtificial intelligence 311 $a1-83969-887-X 327 $a1. 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. 330 $aIn view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data. 410 0$aArtificial intelligence (IntechOpen (Firm)) 606 $aArtificial intelligence 606 $aCluster analysis 615 0$aArtificial intelligence. 615 0$aCluster analysis. 676 $a006.3 702 $aTang$b Niansheng 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910633977103321 996 $aData Clustering$92687086 997 $aUNINA