Intelligent Data Analysis

4Intelligent Data Analysis with Data Mining: Theory and Applications

Shivam Bachhety, Ramneek Singhal, and Rachna Jain

Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India

Objective

Having explained various aspects of intelligent data analysis (IDA) in previous chapters, we are now going to see it along with the essence of data mining. In this chapter we are going to base our discussion on the following major points:

• What data mining is? What are the similarities and differences between data and knowledge?
• Process of knowledge discovery in data. Along with it, various mining methods, including classification, clustering, and decision tree will be discussed.
• Issues related to data mining and its evaluation.
• In addition to that, the chapter will be concluded with a view of data visualization and probability concepts for intelligent data analysis (IDA).

4.1 Introduction to Data Mining

Data mining has been defined as:

. . . the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques [1].

Initially, the term “data fishing” was used, which meant analyzing data without a prior hypothesis. The term was used in a negative way and continued to be used until Michael Lovell published an article in the Review of Economic Studies in 1983. He used the term ...