ProUM: High Utility Sequential Pattern Mining
| Autor: | Philip S. Yu, Hamido Fujita, Jiexiong Zhang, Han-Chieh Chao, Wensheng Gan, Jerry Chun-Wei Lin |
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| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
Sequence Computer science 02 engineering and technology Extension (predicate logic) computer.software_genre Projection (relational algebra) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pruning (decision trees) Data mining Sequential Pattern Mining computer |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2019.8914402 |
| Popis: | In recent decade, utility mining has attracted a great attention, but most of the existing studies are developed to deal with itemset-based data. Different from the itemset-based data, the time-ordered sequence data is more commonly seen in real-world situations. Current utility mining algorithms have the limitation when dealing with sequence data since they are time-consuming and require large amount of memory usage. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store necessary information of sequence-order and utility. By utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption. Besides, we propose a new upper bound named sequence extension utility. Several pruning strategies are further applied to improve the efficiency of ProUM. Experimental results show that the proposed ProUM algorithm significantly outperforms the state-of-the-art algorithms. |
| Databáze: | OpenAIRE |
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