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Approach to determine
Frequent Items Dynamically
HONG-ZHEN ZHENG1,
DIAN-HUI CHU2,
DE-CHEN ZHAN3
1,2College
of Computer Science & Technology,
Harbin Institute of
Technology, Weihai,
264209, China
3College
of Computer Science & Technology,
Harbin Institute of
Technology, Harbin,
150001,China
It proposed a new method for dynamically determining the frequent items at any time in a relation which is undergoing deletion operations as well as inserts. Our methods maintain small space data structures that monitor the transactions on the relation, and, when required, quickly output all frequent items without rescanning the relation in the database. With user-specified probability, all frequent items are correctly reported. The methods rely on ideas from “group testing.” They are simple to implement, and have provable quality, space, and time guarantees. Previously known algorithms for this problem that make similar quality and performance guarantees cannot handle deletions, and those that handle deletions cannot make similar guarantees without rescanning the database. Our experiments with real and synthetic data show that our algorithms are accurate in dynamically tracking the frequent items independent of the rate of insertions and deletion Keywords: Frequent items ,Data mining.
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BibTex: @ARTICLE{P1120545003, AUTHOR = {HONG-ZHEN ZHENG and DIAN-HUI CHU and DE-CHEN ZHAN}, TITLE = {Approach to determine Frequent Items Dynamically},
JOURNAL = {The International Journal of Artificial Intelligence and Machine Learning},
YEAR = {2006}, VOLUME ={06}, ISSUE={1}, PAGES={1--6} } ( |
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