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Conference papers

An Efficient Algorithm to Discover Intra-Periodic Frequent Sequences

Abstract : Sequential pattern mining techniques permit to discover recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. It is a challenging problem since mining algorithms are well known to be both time and memory consuming for large databases. In this paper, we extend the traditional problem of mining frequent sequences with intra-periodicity constraints. Then, we study issues related to intra-periodicity constraints such as search space pruning and partitioning. This study leads to a new efficient algorithm called Intra-Periodic Frequent Sequence Miner (IPFSM). Experimental results confirm the efficiency of IPFSM.
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Submitted on : Tuesday, September 8, 2020 - 12:34:38 AM
Last modification on : Wednesday, October 14, 2020 - 4:10:47 AM
Long-term archiving on: : Friday, December 4, 2020 - 5:23:16 PM

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Edith Belise Kenmogne, Clementin Tayou Djamegni. An Efficient Algorithm to Discover Intra-Periodic Frequent Sequences. CARI 2020 - Colloque Africain sur la Recherche en Informatique et en Mathématiques Apliquées, Oct 2020, Thiès, Senegal. ⟨hal-02932844⟩

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