Efficient Rare Association Rule Mining Algorithm

Data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data stored in repositories, corporate databases, and data warehouses. In Data mining field, the primary task is to mine frequent item sets from a transaction database using Association Rule Mining (ARM).Whereas the extraction of frequent patterns has focused the major researches in association rule mining, the requirements of reliable rules that do not frequently appear is taking an increasing interest in a great number of areas. Rare association rule refers to an association rule forming between frequent and rare items or among rare items. In many cases, the contradictions or exceptions also offers useful associations. Recent researches focus on the discovery of such kind of associations called rare associations. The mining of associations involving rare items is referred as rare association rule mining. Approaches to association rule mining uses single minimum support for identifying frequent associations. To mine interesting rare association rules, single minimum support approaches are not useful. Hence, we propose an algorithm based on MSApriori ,we call this new algorithm as MSApriori_VDB which uses vertical database format. Experimental results shows that our algorithms out performs previous approaches in both memory requirement and execution time by reducing the number of database scans. Keywords—Data Mining, Association rules, Rare items, Rare Association rule mining, MSAPriri_vdb