Introduction to Data Mining
Published on 13. January 2026
Book
Paperback/Softback
396 pages
978-1-77956-302-6 (ISBN)
Description
This book seeks to familiarize students with the fundamental concepts and techniques used in the field of data mining. The content spans a wide range of topics including data preprocessing, classification, clustering, association analysis, and anomaly detection. By combining theoretical insights with practical examples, the book aims to equip students with the skills necessary to extract meaningful patterns and knowledge from large datasets, thus preparing them for challenges in data-driven industries.
More details
Language
English
Place of publication
Canada
Publishing group
Arcler Press
Target group
College/higher education
Product notice
Library binding
Dimensions
Height: 254 mm
Width: 203 mm
ISBN-13
978-1-77956-302-6 (9781779563026)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Bechoo Lal, PhD. became a Member (M) of IAENG: International Association of Engineers, USA with membership (108820) in 2010, a Senior Member (SM) in 2019. He has a doctorate PhD in Computer Science, PhD- Information System from University of Mumbai, Master from Banaras Hindu University (BHU), PGP- Data Science from Purdue University, USA. Currently working as a Associate Professor in Department of Computer Science & Engineering, KLEF- KL University Vijayawada Campus Andhra Pradesh, India. His research areas are data science, big data analytics and Machine Learning.
Content
Introduction To Data Mining
2 Data
3 Exploring Data
4 Classification: Basic Concepts, Decision Trees, And Model Evaluation
5 Alternative Techniques
6 Association Analysis: Basic Concepts And Algorithms
7 Association Analysis: Advanced Concepts
8 Cluster Analysis: Basic Concepts And Algorithms
9 Cluster Analysis: Additional Issues And Algorithms
10 Anomaly Detection
2 Data
3 Exploring Data
4 Classification: Basic Concepts, Decision Trees, And Model Evaluation
5 Alternative Techniques
6 Association Analysis: Basic Concepts And Algorithms
7 Association Analysis: Advanced Concepts
8 Cluster Analysis: Basic Concepts And Algorithms
9 Cluster Analysis: Additional Issues And Algorithms
10 Anomaly Detection