Enhancing the Efficiency in Mining Weighted Frequent Itemsets
Guo-Cheng Lan, Tzung‐Pei Hong, Hong Yu Lee, Shyue-Liang Wang, Chun‐Wei Tsai · 2013
To further enhance the performance of finding weighted frequent item sets, this work presents an effective upper-bound model for reducing unpromising candidates in mining process. To achieve this goal, a projection-based pruning strategy based on our previously proposed model is developed to gradually tighten the upper-bound value for each transaction. The experimental results show that the proposed approach can achieve good performance in efficiency.