Mining Frequent Partite Episodes with Partwise Constraints

In this paper, we study the problem of efficiently mining frequent partite episodes that satisfy partwise constraints from an input event sequence. Through our constraints, we can extract episodes related to events and their precedent-subsequent relations, on which we focus, in a short time. This improves the efficiency of data mining using trial and error processes. A partite episode of length \(k\) is of the form \(P = \langle P_1,\ldots ,P_k\rangle \) for sets \(P_i \; (1 \le i \le k)\) of events. We call \(P_i\) a part of \(P\) for every \(1 \le i \le k\). We introduce the partwise constraints for partite episodes \(P\), which consists of shape and pattern constraints. A shape constraint specifies the size of each part of \(P\) and the length of \(P\). A pattern constraint specifies subsets of each part of \(P\). We then present a backtracking algorithm that finds all of the frequent partite episodes satisfying a partwise constraint from an input event sequence. By theoretical analysis, we show that the algorithm runs in output polynomial time and polynomial space for the total input size. In the experiment, we show that our proposed algorithm is much faster than existing algorithms for mining partite episodes on an artificial and a real-world datasets.