Incremental mining of weighted maximal frequent itemsets from dynamic databases

• We propose an incremental mining algorithm that finds weighted maximal frequent itemsets.
• We devise strategies for guaranteeing correctness of the proposed algorithm.
• We suggest performance improving techniques for the incremental pattern mining.
• We provide extensive, comprehensive performance evaluation results.

Frequent itemset mining allows us to find hidden, important information from large databases. Moreover, processing incremental databases in the itemset mining area has become more essential because a huge amount of data has been accumulated continually in a variety of application fields and users want to obtain mining results from such incremental data in more efficient ways. One of the major problems in incremental itemset mining is that the corresponding mining results can be very large-scale according to threshold settings and data volumes. In addition, it is considerably hard to analyze all of them and find meaningful information. Furthermore, not all of the mining results become actually important information. In this paper, to solve these problems, we propose an algorithm for mining weighted maximal frequent itemsets from incremental databases. By scanning a given incremental database only once, the proposed algorithm can not only conduct its mining operations suitable for the incremental environment but also extract a smaller number of important itemsets compared to previous approaches. The proposed method also has an effect on expert and intelligent systems since it can automatically provide more meaningful pattern results reflecting characteristics of given incremental databases and threshold settings, which can help users analyze the given data more easily. Our comprehensive experimental results show that the proposed algorithm is more efficient and scalable than previous state-of-the-art algorithms.