Research Area:  Machine Learning
Recently, a lot of research work has been proposed in different domains to detect outliers and analyze the outlierness of outliers for relational data. However, while sequence data is ubiquitous in real life, analyzing the outlierness for sequence data has not received enough attention. In this article, we study the problem of mining outlying sequence patterns in sequence data addressing the question: given a query sequence s in a sequence dataset D, the objective is to discover sequence patterns that will indicate the most unusualness (i.e., outlierness) of s compared against other sequences. Technically, we use the rank defined by the average probabilistic strength (aps) of a sequence pattern in a sequence to measure the outlierness of the sequence. Then a minimal sequence pattern where the query sequence is ranked the highest is defined as an outlying sequence pattern. To address the above problem, we present OSPMiner, a heuristic method that computes aps by incorporating several pruning techniques. Our empirical study using both real and synthetic data demonstrates that OSPMiner is effective and efficient.
Author(s) Name:  Tingting Wang , Lei Duan , Guozhu Dong , Zhifeng Bao
Journal name:  ACM Transactions on Knowledge Discovery from Data
Publisher name:  ACM
Volume Information:  Volume 14,Issue 5O,ctober 2020 ,Article No.: 62,pp 1–26
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3399671