Efficient Mining of Outlying Sequence Patterns for Analyzing Outlierness of Sequence Data

Author:

Wang Tingting1,Duan Lei1,Dong Guozhu2,Bao Zhifeng3ORCID

Affiliation:

1. Sichuan University, Sichuan, China

2. Wright State University, Dayton, Ohio

3. RMIT University, Melbourne, Victoria, Australia

Abstract

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.

Funder

National Natural Science Foundation of China

Australian Research Council

Google Faculty Research Award

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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1. RNP-Miner: Repetitive Nonoverlapping Sequential Pattern Mining;IEEE Transactions on Knowledge and Data Engineering;2024

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3. COPP-Miner: Top-K Contrast Order-Preserving Pattern Mining for Time Series Classification;IEEE Transactions on Knowledge and Data Engineering;2024

4. OPR-Miner: Order-Preserving Rule Mining for Time Series;IEEE Transactions on Knowledge and Data Engineering;2023-11-01

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