A Three-way Decision Approach to Incremental Frequent Itemsets Mining
Zhiheng Zhang · Journal of Information and Computational Science · 2014
Frequent itemsets mining has been a hot area of research in recent years. In ecommerce, new data enter the system continually. Therefore people designed incremental frequent itemsets mining algorithms to deal with this situation. In this paper, we propose a three-way decision approach along with a synchronization mechanism to this issue. With this approach, all possible itemsets are divided into three regions, namely the positive, the boundary and the negative region. Itemsets in the positive region are already frequent. Itemsets in the boundary region are infrequent, however may be frequent after data increment in the near future. Itemsets in the negative region will not be frequent even after data increment. Therefore to keep the frequent itemsets up-to-date, one only need to check those in the boundary region, and the runtime is saved. Experimental results from a real-world dataset show that the proposed approach is both efficient and reliable. The synchronization mechanism with the appropriate settings of the boundary and negative region thresholds are also discussed.