SSDP: A Simple Evolutionary Approach for Top-K Discriminative Patterns in High Dimensional Databases

Abstract

It is a great challenge to companies, governments and researchers to extract knowledge in high dimensional databases. Discriminative Patterns (DPs) is an area of data mining that aims to extract relevant and readable information in databases with target attribute. Among the algorithms developed for search DPs, it has highlighted the use of evolutionary computing. However, the evolutionary approaches typically (1) are not adapted for high dimensional problems and (2) have many nontrivial parameters. This paper presents SSDP (Simple Search Discriminative Patterns), an evolutionary approach to search the top-k DPs adapted to high dimensional databases that use only two easily adjustable external parameters.