Autor: |
Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Uday Kiran Rage, Yutaka Watanobe, Incheon Paik |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
|
Zdroj: |
IEEE Access, Vol 11, Pp 12504-12524 (2023) |
Druh dokumentu: |
article |
ISSN: |
2169-3536 |
DOI: |
10.1109/ACCESS.2023.3241313 |
Popis: |
Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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