DocumentCode :
2827007
Title :
Computing Maximum Error and Reduced Threshold of Mining Frequent Patterns in Data Stream
Author :
Hao Guanghao ; Zheng Yongqing ; Cui Lizhen
Author_Institution :
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
fYear :
2009
fDate :
19-20 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
Controlling the space consumption and improving the precision of mining result is two challenges of frequent patterns mining in data stream. The parameter ¿ which denotes the maximum error is widely used to reduce the space consumption. In this paper, we firstly propose a computational strategy for identifying maximum error, consist of resource awareness and polynomial approximate, and then propose a reduced threshold for improving mining accuracy.
Keywords :
computational complexity; data mining; pattern classification; polynomial approximation; computational strategy; data stream; frequent patterns mining; maximum error; mining accuracy; mining frequent patterns; polynomial approximate; reduced threshold; resource awareness; space consumption; Association rules; Computer errors; Computer science; Data mining; Databases; Explosions; Itemsets; Polynomials; Space technology; Tree data structures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
Type :
conf
DOI :
10.1109/ICIECS.2009.5363907
Filename :
5363907
Link To Document :
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