Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrence

• URL: http://arxiv.org/abs/2201.11650v1
• Date: Thu, 27 Jan 2022 17:10:16 GMT
• Title: Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrence
• Authors: Thomas Guyet, Wenbin Zhang and Albert Bifet
• Abstract summary: One of the fundamental research directions is to mine sequential patterns over data streams. The pattern over a window of itemsets stream and their multiple occurrences provides additional capability to recognize the essential characteristics of the patterns. We propose a corresponding efficient sequential miner with novel strategies to prune search space efficiently.
• Score: 11.387440344044315
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The need to analyze information from streams arises in a variety of applications. One of the fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the existence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing items and existence based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding efficient sequential miner with novel strategies to prune search space efficiently. Experiments on both real and synthetic data show the utility of our approach.

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