Mining Generalized Association Rules for Service Recommendations for Digital Home Applications

Association rules can be used for service recommendations for digital home applications. Negative associations, which mean the missing of item-sets may imply the appearance of certain item-sets, highlight the implications of the missing item-sets. Many studies have shown that negative associations are as important as the traditional positive ones in practice. The recommendation can be more personalized with the addition of more generalized association rules comprising both positive and negative association rules. In this paper, an algorithm based on the FP-growth framework is proposed to mine the generalized rules. In contrast to previous discovery of negative association rules using the apriori-like approaches, the proposed algorithm efficiently mines the rules and outperforms the apriori-based approach. The algorithm also scales up linearly with the increase of the database size.