Data Classification and Incremental Clustering in Data Mining and Machine Learning (1st ed. 2022)

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 in 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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	EAI/Springer Innovations in Communication and Computing
Release date:	May 2023
First published:	2022
Authors:	Sanjay Chakraborty • S. K. Hafizul Islam • Debabrata Samanta
Dimensions:	235 x 155mm (L x W)
Pages:	196
Edition:	1st ed. 2022
ISBN-13:	978-3-03-093090-5
Categories:	Books Promotions
LSN:	3-03-093090-4
Barcode:	9783030930905

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