# [1]胡孔法,张长海,陈崚,等.一种面向物流数据分析的路径序列挖掘算法ImGSP[J].东南大学学报(自然科学版),2008,38(6):970-974.[doi:10.3969/j.issn.1001-0505.2008.06.007] 　Hu Kongfa,Zhang Changhai,Chen Ling,et al.ImGSP:a path sequence mining algorithm for product flow analysis[J].Journal of Southeast University (Natural Science Edition),2008,38(6):970-974.[doi:10.3969/j.issn.1001-0505.2008.06.007] 点击复制 一种面向物流数据分析的路径序列挖掘算法ImGSP() 分享到： var jiathis_config = { data_track_clickback: true };

38

2008年第6期

970-974

2008-11-20

## 文章信息/Info

Title:
ImGSP:a path sequence mining algorithm for product flow analysis

1 东南大学经济管理学院, 南京 210096; 2 扬州大学信息工程学院, 扬州 225009
Author(s):
1 School of Economics and Management, Southeast University, Nanjing 210096, China
2 College of Information Engineering, Yangzhou University, Yangzhou 225009, China

Keywords:

TP311;N945
DOI:
10.3969/j.issn.1001-0505.2008.06.007

Abstract:
Currently the data in logistic system is very huge, so the efficiency of mining frequent path sequences needs to be improved. Therefore, an efficient algorithm-ImGSP(improved generalized sequential patterns)for analyzing logistic data is presented. In this method the original database is screened to find the path sequences that is greater than or equal to the candidate sequences in the length, and then generate the candidate sequences through generating the transitional candidate sequences. The experiment results show that the ImGSP algorithm can effectively generate frequent patterns by reducing the volume of sequences, and then find the valuable rules. The method not only reduces the size of scanning database but also reduces the candidate sequences set.

## 参考文献/References:

[1] Han J,Kamber M. Data mining concepts and techniques [M].2nd ed.北京:机械工业出版社,2006:489-513.
[2] Park J S,Psy U.An efficient parallel data mining for association rules[C] //Proc of the 4th on Information and Knowledge Management.New York:ACM Press,1995:31-36.
[3] Cheung D W,Han J,Ng V T,et al.A fast distributed algorithm for mining association rules[C] //Proc of the 4th International Conference on Parallel and Distributed Information Systems.Los Alamitos,USA:IEEE Computer Society Press,1996:31-44.
[4] 倪旻,徐晓飞,邓胜春.基于关联规则的零部件供应商选择优化[J].计算机集成制造系统,2004,10(3):13-16.
Ni Min,Xu Xiaofei,Deng Shengchun.Optimization of components suppliers’ selection based on association rule[J]. Computer Integrated Manufacturing Systems,2004,10(3):13-16.(in Chinese)
[5] Zaki M.Spade:an efficient algorithm for mining frequent sequences [J]. Machine Learning,2001,41(2):31-60.
[6] Pei J,Han J,Pinto H,et al.PrefixSpan:mining sequential patterns efficiently by prefix-projected pattern growth[J].IEEE Transactions on Knowledge & Data Engineering,2004,16(1):1424-1440.
[7] Zhang Changhai,Hu Kongfa,Liu Haidong,et al.FMGSP:an efficient method of mining global sequential patterns[C] //Proc of the 4th International Conference on Fuzzy Systems and Knowledge Discovery.Los Alamitos:IEEE Computer Society,2007:761-765.
[8] 陆介平,杨明,孙志挥,等.快速挖掘全局最大频繁项目集[J].软件学报,2005,16(4):553-560.
Lu Jieping,Yang Ming,Sun Zhihui,et al.Fast mining of global maximum frequent itemsets[J]. Journal of Software,2005,16(4):553-560.(in Chinese)
[9] 胡孔法,张长海,陈崚,等.分布式序列模式挖掘技术研究[J].计算机集成制造系统,2007,13(11):2229-2235.
Hu Kongfa,Zhang Changhai,Chen Ling,et al.Global sequential pattern mining in distributed environment[J]. Computer Integrated Manufacturing Systems,2007,13(11):2229-2235.(in Chinese)
[10] Srikant R,Agrawal R.Mining sequential patterns:generalizations and performance improvements[C] //Proc of 5th International Conference on Extending Database Technology.Heidelberg:Springer,1996:3-17.
[11] 张长海,胡孔法,陈崚.序列模式挖掘算法综述[J].扬州大学学报:自然科学版,2007,10(1):41-46.
Zhang Changhai,Hu Kongfa,Chen Ling.Research on sequential pattern mining algorithms[J]. Journal of Yangzhou University:Natural Science Edition,2007,10(1):41-46.(in Chinese)
-关于“散乱点云去噪算法的研究与实现”一文的撤稿声明
“散乱点云去噪算法的研究与实现”一文刊登在2007年11月20日出版的《东南大学学报(自然科学版)》2007年第37卷第6期第1108-1112页上。因当初对一些特殊数据处理的情况考虑不周,导致部分实验结果出现偏差,实现方法有待进一步研究和验证。为避免误导读者,本文作者现声明撤销此稿,请勿再以任何方式引用该文,欢迎该领域的科研工作者批评指正。
(刘大峰,廖文和,戴宁,程筱胜 )/(2008年11月20日)

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