Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

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