Applying weighted association rules with the consideration of product item relevancy
Liewean Cheng, Su-Chuan Chen, Ja‐Shen Chen · 2009
This study aims to introduce a new concept of weighted association rule mining. The purpose is to discover cross section relationship among items and extract the unknown patterns. We proposed two algorithms called HWA (O) and HWA (P) based on the concept that greater the difference among items in an association rule, the higher the weight score is. Hierarchical weights in HWA (O) are assigned according to the hierarchical levels of the items within the itemset and HWA (P) is assigned by a more sophisticated thought proposed. We compared performance of the number of frequent itemsets, number of rules, and content of rules in HWA (O), HWA (P), and Apriori algorithms. As the result, HWA (P) performs better than two other algorithms. At the end of the research, we provide suggestions for retailing managers based on the discovery in the study. To conclude, the algorithm we proposed can efficiently filter out the minor rules and extracts the implicit and unknown patterns. Marketing managers can make decisions more precisely and satisfy customers' needs at the same time.