A unified view of Automata-based algorithms for Frequent Episode Discovery

TL;DR

This paper provides a unified framework for automata-based algorithms in Frequent Episode Discovery, clarifying their relationships, correctness, and enabling generalizations to partial orders in temporal data mining.

Contribution

It introduces a generic algorithm that encompasses existing methods, offers insights into different frequency notions, and supports generalization to partial order episodes.

Findings

All current algorithms are special cases of the proposed generic algorithm.

The unified view reveals quantitative relationships among different frequency measures.

The framework facilitates correctness proofs and generalizations to partial order episodes.

Abstract

Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various algorithms as we show here. We also point out how this unified view helps us to consider generalization of the algorithm so that they can discover episodes with general partial orders.