Many real world applications of association rule mining from large databases help users make good decisions. However, previous studies produced large numbers of irrelevant patterns, and much time was wasted in finding meaningful rules in large and sparse data sets. This study aims to efficiently discover interesting rules that connote causality between the antecedent and the consequence in a target pattern. In this paper, we propose an improved target-association rule mining method that can remove imprecise patterns, rapidly discover target rules, and apply the method to find associations between socio-economic characteristics and life care needs for new immigrant women in Taiwan. Experimental results show that the proposed method outperforms four other algorithms, namely, Apriori, Apriori-CAR, FP-growth, and DCIP, especially for lower supports. The results also confirm that our approach is practical and effective, with good performance for mining target-association rules in sparsely distributed databases.
