Efficient Classification Rules for Complex Data in Decision Making

Abstract

Information mining in enterprise applications facing challenges due to the complex data distribution in large heterogeneous sources. In such scenario, a single approach or method for mining limited the information needs and it also will be a high processing and time consuming. It is necessary to develop an effective mining approach which can be useful for the real time business requirements and decision making tasks. This paper proposed an efficient classification rules generation mechanism for complex data association and information mining using Multi-Features Patterns Combination (MFPC) method. The approach builds a strong association rule between multi features patterns using Feature reduction which will be used for efficient classification for complex data. The approach is evaluated in comparison with the existing feature reduction and classification approaches and measure the classification accuracy to show the flexibility and capability of the proposed mechanism in data classification.