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
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;
Conference_Titel :
Electronic Computer Technology, 2009 International Conference on
Conference_Location :
Macau
Print_ISBN :
978-0-7695-3559-3
DOI :
10.1109/ICECT.2009.69