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UGMINE: utility-based graph mining
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AI
DOI：10.1007/s10489-022-03385-8.png)
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Abstract
En 中文
Frequent pattern mining extracts most frequent patterns from databases. These frequency-based frameworks have limitations in representing users' interest in many cases. In business decision-making, not all patterns are of the same importance. To solve this problem, utility has been incorporated in transactional and sequential databases. A graph is a relatively complex but highly useful data structure. Although frequency-based graph mining has many real-life applications, it has limitations similar to other frequency-based frameworks. To the best of our knowledge, there is no complete framework developed for mining utility-based patterns from graphs. In this work, we propose a complete framework for utility-based graph pattern mining. A complete algorithm named UGMINE is presented for high utility subgraph mining. We introduce a pruning technique named RMU pruning for effective pruning of the candidate pattern search space that grows exponentially. We conduct experiments on various datasets to analyze the performance of the algorithm. Our experimental results show the effectiveness of UGMINE to extract high utility subgraph patterns.
Keywords:
Pattern mining
Graph mining
High utility pattern mining
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