01137cam2-2200373---450-99000142198020331620120726105022.0000142198USA01000142198(ALEPH)000142198USA0100014219820040213d1962----km-y0itay50------baitaITaf||z|||001yyPugliaOsvaldo BaldacciTorinoUnione tipografico-editrice torinese1962X, 550 p., [7] carte di tav.ill.29 cm<<Le>> regioni d'Italia140010003680692001<<Le>> regioni d'Italia, 14PugliaBNCF914.575BALDACCI,Osvaldo33687ITsalbcISBD990001421980203316III.1. 3656 14(I C Coll. 2/14)12348 L.M.III.1.00312570BKUMASIAV51020040213USA011343PATRY9020040406USA011740COPAT29020050301USA011535ANNAMARIA9020120726USA011050Puglia321304UNISA02568nam 2200421 450 991063397710332120231027112707.01-83969-888-810.5772/intechopen.95124(CKB)5700000000338589(NjHacI)995700000000338589(EXLCZ)99570000000033858920230330d2022 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierData Clustering /edited by Niansheng TangLondon :IntechOpen,2022.1 online resource (126 pages)Artificial intelligence1-83969-887-X 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.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.Artificial intelligence (IntechOpen (Firm))Artificial intelligenceCluster analysisArtificial intelligence.Cluster analysis.006.3Tang NianshengNjHacINjHaclBOOK9910633977103321Data Clustering2687086UNINA01567nas 2200529 a 450 991044925260332120231101153536.01613-3722(OCoLC)18129584(CKB)954925569827(CONSER) 89660022(DE-599)ZDB2051294-6(EXLCZ)9995492556982719880624a19889999 uy engur||||||||HumorBerlin ;New York Mouton de Gruyterc1988-Berlin ;Boston De Gruyter Mouton1 online resource ill. (some col.)"International journal of humor research."Refereed/Peer-reviewed0933-1719 Wit and humorPhilosophyPeriodicalsWit and humorPsychological aspectsPeriodicalsWit and humorSocial aspectsPeriodicalsWit and Humor as TopicPhilosophyPsychologyResearchHumor (grappigheden)gttTijdschriftengttPeriodical.Wit and humorPhilosophyWit and humorPsychological aspectsWit and humorSocial aspectsWit and Humor as Topic.Philosophy.Psychology.Research.Humor (grappigheden)Tijdschriften.152.4JOURNAL9910449252603321Humor261964UNINA