Introduction to data mining addison wesley pdf

Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.

Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

Get BOOK. Steinbach, V. Introduction to Data Mining , Addison-Wesley, Witten, E. Morgan Kaufmann Publishers, Hand, H. Mannila, P. Principles of Data Mining. Jia-Wei Mi , Mr. Hui Sun, and Mr. Fan Yan. Part 1 : Introduction. Part 3 : Data Preprocessing. Part 4 : Association. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis.

The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data.

Classification: Some of the most significant improvements in the text have been in the two chapters on classification. The introductory chapter uses the decision tree classifier for illustration, but the discussion on many topics—those that apply across all classification approaches—has been greatly expanded and clarified, including topics such as overfitting, underfitting, the impact of training size, model complexity, model selection, and common pitfalls in model evaluation.

Almost every section of the advanced classification chapter has been significantly updated. The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. We have added a separate section on deep networks to address the current developments in this area.

The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved.