TABLE OF CONTENTS Sanghamitra Bandyopadhyay and Ujjwal Maulik
Summary. Knowledge discovery and data mining has recently emerged as an important research direction for extracting useful information from vast repositories of data of various types. This chapter discusses some of the basic concepts and issues involved in this process with special emphasis on different data mining tasks. The major challenges in data mining are mentioned. Finally, the recent trends in data mining are described and an extensive bibliography is provided.
The sheer volume and variety of data that is routinely being collected as a consequence of widespread automation is mind-boggling. With the advantage of being able to store and retain immense amounts of data in easily accessible form comes the challenge of being able to integrate the data and make sense out of it. Needless to say, this raw data potentially stores a huge amount of information, which, if utilized appropriately, can be converted into knowledge, and hence wealth for the human race. Data mining (DM) and knowledge discovery (KD) are related research directions that have emerged in the recent past for tackling the problem of making sense out of large, complex data sets.
Traditionally, manual methods were employed to turn data into knowledge. However, sifting through huge amounts of data manually and making sense out of it is slow, expensive, subjective and prone to errors. Hence the need to automate the process arose; thereby leading to research in the fields of data mining and knowledge discovery. Knowledge discovery from databases...
