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008			221203s2022 sz | s |||| 0|eng d
020			_a9783030930882
024	7		_a10.1007/978-3-030-93088-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2022 EB
100	1		_aChakraborty, Sanjay _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685552
245	1	0	_aData Classification and Incremental Clustering in Data Mining and Machine Learning _cby Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta.
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXI, 196 páginas) _b86 ilustraciones, 42 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	1		_aEAI/Springer Innovations in Communication and Computing _x2522-8609
505	0		_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.
520			_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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aIslam, Sk Hafizul _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685553
700	1		_aSamanta, Debabrata, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681079 _d1987-
776	0	8	_iPrinted edition: _z9783030930875
776	0	8	_iPrinted edition: _z9783030930899
776	0	8	_iPrinted edition: _z9783030930905
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-93088-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b12/2022 _dz _eIG _zSI