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http://dx.doi.org/10.17703/JCCT.2022.8.6.647

Mining Frequent Itemsets using Time Unit Grouping  

Hwang, Jeong Hee (Dept. of Computer Software, Namseoul Univ)
Publication Information
The Journal of the Convergence on Culture Technology / v.8, no.6, 2022 , pp. 647-653 More about this Journal
Abstract
Data mining is a technique that explores knowledge such as relationships and patterns between data by exploring and analyzing data. Data that occurs in the real world includes a temporal attribute. Temporal data mining research to find useful knowledge from data with temporal properties can be effectively utilized for predictive judgment that can predict the future. In this paper, we propose an algorithm using time-unit grouping to classify the database into regular time period units and discover frequent pattern itemsets in time units. The proposed algorithm organizes the transaction and items included in the time unit into a matrix, and discovers frequent items in the time unit through grouping. In the experimental results for the performance evaluation, it was found that the execution time was 1.2 times that of the existing algorithm, but more than twice the frequent pattern itemsets were discovered.
Keywords
Temporal Data Mining; Data Mining; Temporal Property; Time Unit; Frequent Itemsets;
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Times Cited By KSCI : 1  (Citation Analysis)
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