An Efficient Hybrid by Partitioning Approach for Extracting Maximal Gradual Patterns in Large Databases (MPSGrite)

Abstract

Since automatic knowledge extraction must be performed in large databases, empirical studies are already showing an explosion in the search space for generalized patterns & even more so for frequent gradual patterns. In addition to this we also observe a generation of a very large number of relevant extracted patterns. Faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space & by the waiting time for detection, for end users, of relevant patterns. the objective is to make decisions or refine their analyzes within a reasonable & realistic time frame. The gradual pattern mining algorithms common in large databases are CPU intensive. It is a question for us of proposing a new approach which allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning data sets. MSPGrite, a new, more efficient hybrid algorithm, is the result of the new method. Experiments on numerous sets of well-known datasets support the suggested method.