Mining Frequent Patterns Partially Devoid of Dissociation with Automated MMS Specification Strategy

Abstract

Mining frequent patterns with single minimum support is quite unpredictable as it may miss rare itemsets depending upon the high value of minimum threshold. The phenomenon is commonly known as “rare item problem” dilemma. In this connection, frequent pattern mining under multiple minimum supports (MMS) is considered as an adequate solution of the problem. Existing approaches in this domain require user-defined minimum item support (MIS), and consider only the support in recognizing the importance of the items and ignore other factors including the inter-item relationships. The generation of huge number of itemsets including the itemsets with low associability or high dissociation is another problem in the existing MMS-based approaches. Keeping view of the above issues, this paper introduces AutoMMS-FPM, an automated MMS specification approach for frequent pattern mining with partial devoid of dissociation. The proposed approach not only takes account of support but also some other factors such as influence (inf), dissociation (d) and length importance factor (ρ) to boost up the mining process. Influence refers to the inter-item relationship and it is formulated based on the concept of degree centrality of the corresponding item network. MIS for the items are calculated automatically using MBS and RIF factors where the first one relates to the item support and the second one relates to the item influence. Experimental results on both of the synthetic and real datasets show that the proposed algorithm outperforms than existing approaches in terms of number of generated itemsets, run time, memory usages, dissociation and scalability.

Topics & Concepts