Abstract: Erasable itemset mining is an approach of mining low profit itemset from large-scale pro- duct databases in order to solve the financial crisis of manufactures. Traditional erasable itemset mining methods deal with static product databases only, and ignore the weight of item itself when they extract the erasable itemset. To address the problem of single condition and the inefficiency of existing erasable itemset mining algorithms, an effective algorithm WELI is proposed to mine erasable itemset in an incremental database with weighted condition. The proposed algorithm comprehensively considers the factors of data accumulation and different importance of items. The concise list structure is applied to reduce the memory consumption. Besides, the proposed algorithm prunes the invalid itemset with the weight conditions. Whats more, it can simplify the process of gain calculation by combining subsume index and difference set. Therefore, it can achieve incremental mining operations efficiently. Experiments show that, in terms of running time and memory consumption, the algorithm has good experimental results on both dense and sparse data sets. In terms of scalability, the algorithm is also superior to previous algorithms.

Key words: data mining, erasable itemset, incremental mining, weighted conditions, subsume index