Efficiently Updating the Discovered Sequential Patterns for Sequence Modification

Abstract

Mining sequential patterns (SPs) is a popular data mining task, which consists in finding interesting, unexpected, and useful patterns in sequence databases. It has several applications in many domains. However, most sequential pattern mining algorithms assume that databases are static, i.e. that they do not change over time. But in real-word applications, sequences are often modified. Thus, it is an important issue to design algorithms for updating SPs in a dynamic database environment. Although some algorithms have been proposed to maintain SPs in dynamic databases, these algorithms may have poor performance, especially when databases contain long sequences or a large number of sequences. This paper addresses this issue by proposing a novel dynamic mining approach named PreFUSP-TREE-MOD to address the problem of maintaining and updating discovered SPs when sequences in a database are modified. The proposed approach adopts the previously proposed pre-large concept using two support thresholds, to avoid scanning the database when possible, for updating the set of discovered patterns. Due to the pruning properties of the pre-large concept, the PreFUSP-TREE-MOD maintenance algorithm can effectively reduce the cost of database scans to maintain and update the built FUSP-tree for sequence modification. When the number of modified sequences is less than the safety bound of the pre-large concept, the proposed maintenance algorithm outperforms traditional SPM algorithms in batch mode, and the state-of-the-art maintenance algorithm in terms of execution time and number of tree nodes.