LEADER 04348nam 22006495 450 001 9910568253803321 005 20250626164053.0 010 $a3-030-93088-2 024 7 $a10.1007/978-3-030-93088-2 035 $a(MiAaPQ)EBC6986477 035 $a(Au-PeEL)EBL6986477 035 $a(CKB)22371878700041 035 $a(DE-He213)978-3-030-93088-2 035 $a(EXLCZ)9922371878700041 100 $a20220510d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Classification and Incremental Clustering in Data Mining and Machine Learning /$fby Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (210 pages) 225 1 $aEAI/Springer Innovations in Communication and Computing,$x2522-8609 311 08$aPrint version: Chakraborty, Sanjay Data Classification and Incremental Clustering in Data Mining and Machine Learning Cham : Springer International Publishing AG,c2022 9783030930875 327 $aIntroduction to Data Mining & Knowledge Discovery -- A Brief Concept on Machine Learning -- Supervised Learning based Data Classification and Incremental Clustering -- Data Classification and Incremental Clustering using Unsupervised Learning -- Research Intention towards Incremental Clustering -- Applications and Trends in Data Mining & Machine Learning -- Feature subset selection techniques with Machine Learning -- Data Mining Based variant subsets features. 330 $aThis book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques. Provides a comprehensive review of various data mining techniques and architecture, primarily focusing on supervised and unsupervised learning Presents hands-on coding examples using three popular coding platforms: R, Python, and Java Includes case-studies, examples, practice problems, questions, and solutions for students and professionals, focusing on machine learning and data science. 410 0$aEAI/Springer Innovations in Communication and Computing,$x2522-8609 606 $aTelecommunication 606 $aComputational intelligence 606 $aComputer vision 606 $aData mining 606 $aCommunications Engineering, Networks 606 $aComputational Intelligence 606 $aComputer Vision 606 $aData Mining and Knowledge Discovery 615 0$aTelecommunication. 615 0$aComputational intelligence. 615 0$aComputer vision. 615 0$aData mining. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aComputer Vision. 615 24$aData Mining and Knowledge Discovery. 676 $a006.312 676 $a006.31 700 $aChakraborty$b Sanjay$01228885 702 $aIslam$b Sk Hafizul 702 $aSamanta$b Debabrata 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910568253803321 996 $aData classification and incremental clustering in data mining and machine learning$92986729 997 $aUNINA