Efficient mining of intra-periodic frequent sequences

Frequent Sequence Mining (FSM) is a fundamental task in data mining. Although FSM algorithms extract frequent patterns, they cannot discover patterns that periodically appear in the data. However, periodic trends are found in many areas such as market basket analysis, where discovering itemsets peri...

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Bibliographic Details
Published inArray (New York) Vol. 16; p. 100263
Main Authors Kenmogne, Edith Belise, Tayou Djamegni, Clémentin, Nkambou, Roger, Tabueu Fotso, Laurent Cabrel, Tadmon, Calvin
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.12.2022
Elsevier
Subjects
Inclusion-carried mapping
Intra-periodicity
Mining
Partitioning
Pruning
Search space
Sequence irreducibility
Mining
Inclusion-carried mapping
Sequence irreducibility
Partitioning
Pruning
Intra-periodicity
Search space
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Summary:Frequent Sequence Mining (FSM) is a fundamental task in data mining. Although FSM algorithms extract frequent patterns, they cannot discover patterns that periodically appear in the data. However, periodic trends are found in many areas such as market basket analysis, where discovering itemsets periodically purchased by customers can help understand periodic customer behavior. This is the task of Periodic Frequent Pattern Mining (PFPM). A major limitation common to traditional PFPM algorithms is that they reduce the periodicity between non-disjoint itemsets. They do not take into account the periods between disjoint itemsets. Thus, they find itemsets that appear periodically, but would fail to find a periodic appearance of distinct itemsets. To address this limitation, this paper extends the traditional problem of FSM with intra-periodicity and provides a theoretical background to extract intra-periodic frequent sequences. This leads to a new mining algorithm called Intra-Periodic Frequent Sequence Miner. Experimental results confirm its efficiency. •Intra-periodicity is integrated into the problem of mining frequent sequences.•A theoretical background for the extraction of intra-periodic frequent sequences.•New pruning and partitioning strategies of the search space.•A new algorithm called Intra-Periodic Frequent Sequence Miner (IPFSM).•The correctness of IPFSM is proven and experiments confirm its efficiency.
ISSN:2590-0056
2590-0056
DOI:10.1016/j.array.2022.100263