Abstract
Data mining that discovers knowledge from large datasets is more and more popular in artificial intelligence. In recent years, the problem of mining erasable patterns (EPs) has been proposed as an interesting variant of frequent pattern mining. There are many algorithms for solving effectively the problem of mining EPs. However, for very big datasets, the large number of EPs takes the large memory usage of the system, and then obstructs users’ using the system. Therefore, it is necessary to mine a condensed representation of EPs. In this paper, we present the erasable closed patterns (ECPs) concept and an effective algorithm for mining ECPs (MECP algorithm). The experimental results show that the number of ECPs is much less than that of EPs. Besides, the runtime of MECP is better than the naïve approach for mining ECPs.
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Nguyen, G., Le, T., Vo, B., Le, B. (2015). Discovering Erasable Closed Patterns. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_36
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DOI: https://doi.org/10.1007/978-3-319-15702-3_36
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