Introduction

Abstract

Data mining aims at generating abstraction from large datasets through efficient algorithms. Some approaches to achieve efficiency are to arrive at valid representative subsets of original data and feature sets. All further data mining analysis can be based only on these representative subsets leading to significant reduction in storage space and time. Another important direction is to compress the data by some manner and operate in the compressed domain directly. In this chapter, we present a discussion on major data mining tasks such as clustering, classification, dimensionality reduction, association rule mining, and data compression; all these tasks may be viewed as some kind of data abstraction or compaction tasks. We further discuss various aspects of compression schemes both in abstract sense and as practical implementation. We provide a brief summary of content of each chapter of the book and discuss its overall organization. We provide literature for further study at the end.