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 and 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. Being faced with this problem, many approaches have been developed, with the aim of reducing the size of the search space and the waiting time for detection, for end users, of relevant patterns. The objective is to make decisions or refine their analyses within a reasonable and 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 that allows an extraction of the maximum frequent gradual patterns based on a technique of partitioning datasets. The new technique leads to a new, more efficient hybrid algorithm called MSPGrite. The experiments carried out on several sets of known datasets justify the proposed approach.