# [1]吉根林,孙志挥.一种基于可信度最优的数量关联规则挖掘算法[J].东南大学学报(自然科学版),2001,31(2):31-34.[doi:10.3969/j.issn.1001-0505.2001.02.008] 　Ji Genlin,Sun Zhihui.An Algorithm for Mining Optimized Confidence Quantitative Association Rules[J].Journal of Southeast University (Natural Science Edition),2001,31(2):31-34.[doi:10.3969/j.issn.1001-0505.2001.02.008] 点击复制 一种基于可信度最优的数量关联规则挖掘算法() 分享到： var jiathis_config = { data_track_clickback: true };

31

2001年第2期

31-34

2001-03-20

## 文章信息/Info

Title:
An Algorithm for Mining Optimized Confidence Quantitative Association Rules

1 南京师范大学计算机科学系,南京 210097; 2 东南大学计算机科学与工程系,南京 210096
Author(s):
1 Department of Computer Science, Nanjing Normal University, Nanjing 210097; 2 Department of Computer Science and Engineering, Southeast University, Nanjing 210096)

Keywords:

TP311
DOI:
10.3969/j.issn.1001-0505.2001.02.008

Abstract:
This paper discusses the problem of discretization for continuous attributes and describes a method for discretization in the processing of mining quantitative association rules, including quantitative ranges partitioning and sampling to a huge database. An algorithm for mining optimized confidence quantitative association rules is presented. In the algorithm, the equi-depth partitioning is used to discrete for continuous attributes and a technique of handing convex hulls is used to compute optimized confidence quantitative association ranges. Given a huge database, we address the problem of finding association rules for numeric attributes, such as (A∈[v1,v2])C, in which C is boolean attribute. Our goal is to realize a system that finds an appropriate range automatically. We use the algorithms to analyse the buying and selling of stocks, finding association rules between stock price and fluctuation of price. The experiment states clearly that the algorithms are correct.

## 参考文献/References:

[1] Fayyad U M,Piatetsky-Shapiro G,Smyth P.Advance in knowledge discover and data mining.California:AAAI Press,The MIT Press,1996.1～25
[2] Srikant R,Agrawal R.Mining quantitative association rules in large relational table.In:Carey M,Schneider D,eds.Proceedings of the ACMSIGMOD Conference on Management of Data.New York:ACM Press,1996.1～12
[3] Fukuda T,Morimoto Y,Morishita S,et al.Mining optimized association rules for numeric attributes.In:Mendelzon A,Ozsoyoglu Z,eds.Proceeding of the 15th ACM Symposium on Principles of Database Systems.New York:ACM Press,1996.182～191
[4] Fukuda T,Morimoto Y,Morishita S,et al.Data mining using two-dimensional optimized association rules.In:Carey M,Schneider D,eds.Proceedings of the ACMSIGMOD Conference on Management of Data.New York:ACM Press,1996.13～24
[5] Robert Groth.Data mining:building competitive advantage.New Jersey:Prentice Hall PTR,2000.28～30
[6] 苑森淼,程晓青.数量关联规则发现中的聚类方法研究.计算机学报,2000,23(8):866～871

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