Multiple criteria linear programming approach to data mining: models, algorithm designs and software development

It is well known that data mining has been implemented by statistical regressions, induction decision tree, neural networks, rough set, fuzzy set, etc. This paper promotes a multiple criteria linear programming (MCLP) approach to data mining based on linear discriminant analysis. This paper first describes the fundamental connections between MCLP and data mining, including several general models of MCLP approaches. Given the general models, it focuses on a designing architecture of MCLP-data mining algorithms in terms of a process of real-life business intelligence. This architecture consists of finding MCLP solutions, preparing mining scores, and interpreting the knowledge patterns. Secondly, this paper elaborates the software development of the MCLP-data mining algorithms. Based on a pseudo coding, two versions of software (SAS- and Linux-platform) will be discussed. Finally, the software performance analysis over business and experimental databases is reported to show its mining and prediction power. As a part of the performance analysis, a series of data testing comparisons between the MCLP and induction decision tree approaches are demonstrated. These findings suggest that the MCLP-data mining techniques have a great potential in discovering knowledge patterns from a large-scale real-life database or data warehouse.