Abstract
Periodic-frequent pattern mining involves finding all frequent patterns that have occurred at regular intervals in a transactional database. The basic model considers a pattern as periodic-frequent, if it satisfies the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. The usage of a single minSup and maxPer for an entire database leads to the rare-item problem. When confronted with this problem in real-world applications, researchers have tried to address it using the item-specific minSup and maxPer constraints. It was observed that this extended model still generates a significant number of uninteresting patterns, and moreover, suffers from the issue of specifying item-specific minSup and maxPer constraints. This paper proposes a novel model to address the rare-item problem in periodic-frequent pattern mining. The proposed model considers a pattern as interesting if its support and periodicity are close to that of its individual items. The all-confidence is used as an interestingness measure to filter out uninteresting patterns in support dimension. In addition, a new interestingness measure, called periodic-all-confidence, is being proposed to filter out uninteresting patterns in periodicity dimension. We have proposed a model by combining both measures and proposed a pattern-growth approach to resolve the rare-item problem and extract interesting periodic-frequent patterns. Experimental results show that the proposed model is efficient.
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Venkatesh, J.N., Uday Kiran, R., Krishna Reddy, P., Kitsuregawa, M. (2016). Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9827. Springer, Cham. https://doi.org/10.1007/978-3-319-44403-1_4
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