NDLI: Frequent items mining on data stream using hash-table and heap

Content Provider	IEEE Xplore Digital Library
Author	Zhang Shan Chen Ling Tu Li
Copyright Year	2009
Description	Author affiliation: Institute of Information Science and Technology, Naming University of Aeronautics and Astronautics, China (Tu Li) \|\| Department of Computer Science, Yang Zhou University, China (Zhang Shan; Chen Ling)
Abstract	Most of the existing algorithms for mining frequent items on data stream do not emphasis the importance of the recent data items. We present an algorithm to detect the items with frequency counts exceeding a user-specified threshold. Our algorithm uses a hash table L and a heap to record the potential frequent items, and can detect ε-approximate frequent data items on data stream using O(\|L\|+ $ε^{−1})$ memory space and the processing time for each data item is $O(logε^{−1}).$ Experimental results on several artificial and real datasets show our algorithm has higher precision, requires less memory and consumes less computation time than other similar methods.
Starting Page	141
Ending Page	145
File Size	223539
Page Count	5
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5357918
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fading Area measurement Extraterrestrial measurements Frequent items Data mining Hash table Heap Computer science Information science Data stream Space technology Time fading model Computer errors Frequency Sampling methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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