Rare association rule mining from incremental databases

Abstract

Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques.