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Data classification and incremental clustering in data mining and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data classification and incremental clustering in data mining and machine learningby Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta.
作者:	Chakraborty, Sanjay.
其他作者:	Islam, Sk Hafizul.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxi, 196 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-030-93088-2
ISBN:	9783030930882$q(electronic bk.)

Data classification and incremental clustering in data mining and machine learning
Chakraborty, Sanjay.

Data classification and incremental clustering in data mining and machine learning[electronic resource] /by Sanjay Chakraborty, Sk Hafizul Islam, Debabrata Samanta. - Cham :Springer International Publishing :2022. - xxi, 196 p. :ill. (some col.), digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8609. - EAI/Springer innovations in communication and computing..

Introduction 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.

This 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.

ISBN: 9783030930882$q(electronic bk.)

Standard No.: 10.1007/978-3-030-93088-2doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / C43 2022

Dewey Class. No.: 006.312

Data classification and incremental clustering in data mining and machine learning
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520 $a This 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.
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