Mining recent maximal frequent itemsets over data streams with sliding window
Joint Authors
Cai, Saihua
Hao, Shangbo
Sun, Ruizhi
Wu, Gang
Source
The International Arab Journal of Information Technology
Issue
Vol. 16, Issue 6 (30 Nov. 2019), pp.961-969, 9 p.
Publisher
Publication Date
2019-11-30
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The huge number of data streams makes it impossible to mine recent frequent itemsets.
Due to the maximal frequent itemsets can perfectly imply all the frequent itemsets and the number is much smaller, therefore, the time cost and the memory usage for mining maximal frequent itemsets are much more efficient.
This paper proposes an improved method called Recent Maximal Frequent Itemsets Mining (RMFIsM) to mine recent maximal frequent itemsets over data streams with sliding window.
The RMFIsM method uses two matrixes to store the information of data streams, the first matrix stores the information of each transaction and the second one stores the frequent 1-itemsets.
The frequent p-itemsets are mined with “extension” process of frequent 2-itemsets, and the maximal frequent itemsets are obtained by deleting the sub-itemsets of long frequent itemsets.
Finally, the performance of the RMFIsM method is conducted by a series of experiments, the results show that the proposed RMFIsM method can mine recent maximal frequent itemsets efficiently.
American Psychological Association (APA)
Cai, Saihua& Hao, Shangbo& Sun, Ruizhi& Wu, Gang. 2019. Mining recent maximal frequent itemsets over data streams with sliding window. The International Arab Journal of Information Technology،Vol. 16, no. 6, pp.961-969.
https://search.emarefa.net/detail/BIM-915139
Modern Language Association (MLA)
Cai, Saihua…[et al.]. Mining recent maximal frequent itemsets over data streams with sliding window. The International Arab Journal of Information Technology Vol. 16, no. 6 (Nov. 2019), pp.961-969.
https://search.emarefa.net/detail/BIM-915139
American Medical Association (AMA)
Cai, Saihua& Hao, Shangbo& Sun, Ruizhi& Wu, Gang. Mining recent maximal frequent itemsets over data streams with sliding window. The International Arab Journal of Information Technology. 2019. Vol. 16, no. 6, pp.961-969.
https://search.emarefa.net/detail/BIM-915139
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 968-969
Record ID
BIM-915139