APRIORI MULTIPLE ALGORITHM FOR MINING ASSOCIATION RULES

被引：0

作者：

Stanisic, Predrag ^{[1
]}

Tomovic, Savo ^{[1
]}

机构：

[1] Univ Montenegro, Podgorica, Montenegro

来源：

INFORMATION TECHNOLOGY AND CONTROL | 2008年 / 37卷 / 04期

关键词：

data mining; knowledge discovery in databases; association analysis; Apriori algorithm;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

One of the most important data mining problems is mining association rules. In this paper we consider discovering association rules from large transaction databases. The problem of discovering association rules can be decomposed into two sub-problems: find large itemsets and generate association rules from large itemsets. The second sub-problem is easier one and the complexity of discovering association rules is determined by complexity of discovering large itemsets. In this paper, we suggest Apriori-based algorithm for discovering large itemsets. Actually, we suggest a new procedure for large itemsets generation which is more efficient than the appropriate procedure of the original Apriori algorithm. For its implementation, we suggest a modified sort-merge-join algorithm, which is more efficient than nested-loop-join algorithm, which is suggested in the original Apriori algorithm. Besides, we propose a

引用

页码：311 / 320

页数：10

共 25 条

[21]

Silberschatz A., 2006, DATABASE SYSTEM CONC, V3rd

[22]

Tan PN, 2006, INTRO DATA MINING

[23]

Toivonen H, 1996, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P134

[24]

Zaki M. J., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, P283

[25]

Zhu J., 2003, Efficiently using prefix-trees in mining frequent itemsets, Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations

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