Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases

Abstract

Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior in a temporal database. The following two limitations have hindered the successful industrial application of this technique: (i) there exists no algorithm to find the desired patterns in columnar temporal databases, and (ii) existing algorithms are computationally expensive both in terms of runtime and memory consumption. This paper tackles these two challenging problems by proposing a novel algorithm known as partial periodic-frequent depth-first search (PPF-DFS). The proposed algorithm compresses a given row or columnar temporal database into a unified dictionary structure and mines this structure recursively to find all desired patterns. Experimental results demonstrate that PPF-DFS is 2 to 8.8 times faster and 5 to 31 times more memory efficient than the state-of-the-art algorithm.

First three authors have equally contributed to 90% of the work