DocumentCode
2921369
Title
Finding Frequent Item Sets from Sparse Matrix
Author
Zheng Xiao-yan ; Sun Ji-Zhou
Author_Institution
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin
fYear
2009
fDate
20-22 Feb. 2009
Firstpage
615
Lastpage
619
Abstract
According to the features of sparse data source while mining association rules, the paper designs a special linked-list unit and two strategies to store data in matrix. A novel algorithm, called SMM (Sparse-Matrix Mining), is proposed to find large item sets from sparse matrix. SMM maps database into a binary sparse matrix and stores compressed data into a linked-list, from which to find large item sets. It uses less I/O and computational time in mining. Experiments show that SMM finds large item sets efficiently and is well scalable.
Keywords
data compression; data mining; set theory; sparse matrices; binary sparse matrix; computational time; frequent item sets; sparse data source; sparse-matrix mining; Association rules; Computer science; Data mining; Educational technology; Electronic mail; Itemsets; Paper technology; Sparse matrices; Sun; Transaction databases; Compress by Column; frequent item sets; linked-list; sparse matrix;
fLanguage
English
Publisher
ieee
Conference_Titel
Electronic Computer Technology, 2009 International Conference on
Conference_Location
Macau
Print_ISBN
978-0-7695-3559-3
Type
conf
DOI
10.1109/ICECT.2009.69
Filename
4796037
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