MapReduce Based Multilevel Consistent and Inconsistent Association Rule Detection from Big Data Using Interestingness Measures

Multilevel association rule mining in distributed environment plays an important role in big data analysis for making marketing strategy. Multilevel association rule provides more significant information than single level rule, and also discovers the conceptual hierarchy of knowledge from the hierarchical dataset. In this era of internet, various online marketing sites and social networking sites are generating enormous amount of data so that it becomes very difficult to process and analyze it using conventional approaches as it consumes more time. This paper overcomes the computing limitation of single node by distributing the task on multi-node cluster. The proposed method initially extracts multilevel association rules including level-crossing for each zone using distributed multilevel frequent pattern mining algorithm (DMFPM). These generated multilevel association rules are so large that it becomes complex to analyze it. Thus, MapReduce based multilevel consistent and inconsistent rule detection (MR-MCIRD) algorithm is proposed to detect the consistent and inconsistent multilevel rules from big hierarchical data which provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy. The extracted multilevel consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.