Introduction to Data Mining  (English, Paperback, Tan Pang-Ning)

Introduction To Data Mining, written by Pang-Ning Tan, Vipin Kumar, and Michael Steinbach, introduces the readers to the fundamental algorithms and concepts of Data Mining.

Summary Of The Book

Data Mining is a technology that blends traditional methods of data analysis with sophisticated algorithms for processing vast amounts of data.

In Introduction To Data Mining, the authors start with the basic concepts of data mining, and then proceed to the more complex techniques and algorithms. Every concept is dealt with in great detail. The authors have also used many examples to make the concepts easier to grasp.

The book contains 10 chapters. The first chapter titled Introduction, gives an overview of data mining, and outlines the topics that are covered in the book. The second chapter, Data, deals with Types of Data, Quality of Data, Data Preprocessing, and Measures of Similarity and Dissimilarity.

The other chapters in the book are Classification: Basic Concepts, Decision Trees and Model Evaluation, Exploring Data, Association Analysis: Basic Concepts and Algorithms, Classification: Alternative Techniques, Cluster Analysis: Additional Issues and Algorithms, Cluster Analysis: Basic Concepts and Algorithms, and Association Analysis: Advanced Concepts. The authors cover topics like Rule Based Classifier, Nearest-Neighbor Classifier, Artificial Neural Network, and other topics as well. The last chapter, Anomaly Detection, is followed by Appendices 1 to 5. These are Dimensionality Reduction, Linear Algebra, Probability and Statistics, Optimization and Regression.

Each chapter ends with a set of exercises on the topics that have been covered in that particular chapter. The authors have given theoretical as well as practical coverage of all the topics. The extensive use of figures and examples facilitates the understanding of the topics.

About The Authors

Pang-Ning Tan is an associate professor at Michigan State University’s Department of Computer Science and Engineering. Tan’s research in the field of data mining has been supported by National Aeronautics and Space Administration, the National Institutes of Health, the Office of Naval Research, and the Army Research Office. He has more than 100 published papers in journals, conferences, and workshops.

Vipin Kumar is the professor and Head of the Computer Science and Engineering Department, University of Minnesota. Kumar has co-authored several books. His other books include Introduction to Parallel Computing, Parallel Processing for Artificial Intelligence and Computational Approaches for Protein Function Prediction. Vipin Kumar did his BE in Electronics and Communication Engineering from IIT Roorkee. He did his ME in Electronics Engineering from Netherlands, and his Ph.D. in Computer Science from the University of Maryland. Kumar is the co-founder of the SIAM International Conference on Data Mining. He has received several awards, including the ACM SIGKDD 2012 Innovation Award, the highest award for technical expertise in Knowledge Discovery and Data Mining. He also has over 250 research articles to his credit.

Michael Steinbach is a research associate and author. Michael Steinbach did his BS in Mathematics, MS in Statistics, MS and Ph.D. in Computer Science, all from the University of Minnesota. Steinbach is a research associate in the Department of Computer Science and Engineering, University of Minnesota. He has also written over 20 research articles.