An efficient algorithm for association mining

被引：1

作者：

Jin, Kan ^{[1
]}

机构：

[1] Jinan Univ, Dept Software Engn, Guangzhou, Guangdong, Peoples R China

来源：

2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 1 | 2009年

关键词：

Data mining; association rules; Apriori algorithm; frequent patterns;

D O I：

10.1109/KAM.2009.55

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Association rule discovery plays an important role in knowledge discovery and data mining, and efficiency is especially crucial for an algorithm to find frequent patterns from a large database. In this paper, a new algorithm called LogApriori algorithm is proposed by the idea of reducing unnecessary scanning of database in Apriori algorithm. The correctness of LogApriori algorithm is proved in this paper, and the performance of LogApriori algorithm is better than Apriori algorithm theoretically and practically. The success of LogApriori algorithm indicates that the strategy of producing itemsets with different number of items in one scanning can indeed find frequent patterns correctly and effectively.

引用

页码：291 / 295

页数：5

共 8 条

[1]

Agarwal R., 1994, VLDB, V487, P499, DOI DOI 10.5555/645920.672836

[2]

Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072

[3] Frequent pattern mining: current status and future directions [J].

Han, Jiawei ;

Cheng, Hong ;

Xin, Dong ;

Yan, Xifeng .

DATA MINING AND KNOWLEDGE DISCOVERY, 2007, 15 (01) :55-86

[4]

Han Jiawei., 2006, Data mining: concepts and techniques, P227

[5]

Han Jiawei., 2000, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, P355

[6]

Liu Z, 2007, ANAL OPTIMIZATION AP

[7] Average-case performance of the Apriori Algorithm [J].

Purdom, PW ;

Van Gucht, D ;

Groth, DP .

SIAM JOURNAL ON COMPUTING, 2004, 33 (05) :1223-1260

[8] Scalable algorithms for association mining [J].

Zaki, MJ .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2000, 12 (03) :372-390

← 1 →