Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
This work addresses a gap in sequential pattern mining for data streams, enabling better recognition of pattern characteristics and inter-relationships, though it appears incremental as it builds on existing mining approaches.
The paper tackles the problem of mining sequential patterns from data streams by considering multiple occurrences of itemsets, which existing presence-based methods ignore, and proposes a novel miner with efficient pruning strategies that shows utility on real and synthetic data.
The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence 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 presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach.