Incremental clickstream pattern mining with search boundaries

Abstract

Recently, there has been a growing interest in sequential pattern mining in data mining, with a particular focus on clickstream pattern mining. These areas hold the potential for discovering valuable patterns. However, traditional mining algorithms in these domains often assume that databases are static, simplifying the mining process. In reality, databases are updated incrementally over time, partially rendering a portion of the previous results invalid. This necessitates rerunning algorithms on updated databases to obtain accurate frequent patterns. As database size increases, this approach can become time-consuming and affect performance. To tackle this issue, we propose PSB-CUP to mine frequent clickstream patterns in an incremental update manner. PSB-CUP employs the concept of search borders to reduce the search space and the information retained in memory. Furthermore, an IDList generation method called “partial imbalance join” was proposed to reconstruct possibly missing information during the incremental process. This join method, however, requires more extra information to be cached in exchange for speed. We then improve this technique by introducing “recursive imbalance join”, removing the need for extra cached data in the PSB-CUP + algorithm. The experimental results show that our proposed algorithms are efficient for incremental clickstream pattern mining.