2010/07/05 by Avinash Achar, Achar, Avinash, Srivatsan Laxman +3 · 1 citation
Computer Science · #Algorithms and Data Compression #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Data Mining Algorithms and Applications #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1007.0690
openalex publication_date 2010/07/05 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.