Parallel Rule Generation for Making an Efficient Classification System

Nowadays, size of databases is increasing drastically which requires huge memory and high computational power to overcome memory and computational limitations efficiently. To increase performance and overcome memory limitation we need distributed approach. In this paper, a three step distributed approach is proposed which divides the large data sets into data chunks initially, processes it on defined N processors on different machines, generates the final merged decision rule file and resolves the conflicts that may arise later on. Mostly, classification algorithms generates only specific or generic decision rules, in contrast to traditional algorithms proposed solution has capability to generate both specific and generic rules. This approach shows promising results in terms of accuracy and efficiency and well suited for distributed environment.