Cat Swarm Optimization Supported Data Mining

Introduction

Data Mining (DM) is a series of processes, which analyses the data and sieves some useful information or interesting knowledge out from real-world large and complex data sets (Ghosh & Jain, 2005). Various statistics, analysis, and modeling methods are employed to find patterns and relationships in DM. The process of Knowledge Discovery from Data (KDD) is the key, which makes the outcome of DM being meaningful. Nevertheless, the fast development of computer science, the database management system (DBMS) and Data Warehouse (DW) pushes the size of the datasets increases forward with an astounding speed. It results in that precisely extracting the knowledge or finding the relationships and patterns become more difficult. Hence, the need for powerful tools to assist DM is clear. To build such tools for assisting DM, one of the ways is to lead in the evolutionary computing.

In this chapter, the concept of DM is reviewed, and several algorithms in evolutionary computing are presented. In addition, a feasible solution, which combines the intelligent computing and DM, is proposed. Finally, the discussion that concludes the chapter is made.

Concept Of Data Mining

Data mining is an essential step in the process of KDD as depicted in Figure 1 (Han & Kamber, 2007). By using data mining as a tool, several models, which are used to simulate the situations in the real world, can be created to describe the relationships between social behaviors in the real world and the patterns observed in the data. These patterns and relationships are very useful for increasing enterprise profit. For instance, the patterns and their relations to the actual shopping behaviors in customers help the store owners understand how to place their goods to make the customers feel more convenient when shopping. Hence, discovering the knowledge, which is hidden in the data, via data mining is very important.

Figure 1.

Data mining as a step in the process of knowledge discovery.

In data mining, we can create 6 kinds of model to assist us to discover knowledge from the data. The models are listed as follows:

• 1.

  Regression.

• 2.

  Classification.

• 3.

  Time-Series Forecasting.

• 4.

  Clustering.

• 5.

  Association Rule.

• 6.

  Sequence Discovery.

No matter which model is employed, the training data is required to construct the knowledge from mining. The training data may comes from the historical records in the database or simply separates the data into two parts. One part of the data is used as the training dataset, and the other part of the data is used as the test dataset.