A sparse memory allocation data structure for sequential and parallel association rule mining

被引：10

作者：

Soysal, Oemer M. ^{[1
]}

Gupta, Eera ^{[1
]}

Donepudi, Harisha ^{[1
]}

机构：

[1] Louisiana State Univ, Highway Safety Res Grp, 3535 Nicholson Ext, Baton Rouge, LA 70803 USA

来源：

JOURNAL OF SUPERCOMPUTING | 2016年 / 72卷 / 02期

关键词：

Association rule mining; Multi-thread; Multi-core; Parallel computing; Apriori; Fp-growth; Sparse memory allocation; FREQUENT; ALGORITHMS; DISCOVERY; ITEMSETS;

D O I：

10.1007/s11227-015-1566-x

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a sparse memory allocation data structure for sequential and parallel data mining. We explored three algorithms utilizing the proposed data structure: MASP-tree, apriori-TID, and FP-growth. We modified the data structure of apriori-TID and FP-growth algorithms to reduce memory allocation cost. Five data sets are used for comparison. The results show that the modified apriori-TID has a higher speed-up than the modified FP-growth when the proposed data structure is used. A maximum speed-up of 3.42 is observed when MASP algorithm is tested.

引用

页码：347 / 370

页数：24

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