Please wait a minute...
New Technology of Library and Information Service  2008, Vol. 24 Issue (9): 70-77    DOI: 10.11925/infotech.1003-3513.2008.09.12
Current Issue | Archive | Adv Search |
Clustering Analysis of E-commerce Transactions with Self-Organizing Map
Li Gang  An Lu
(Information Management School, Wuhan University, Wuhan 430072, China)
Export: BibTeX | EndNote (RIS)      

To improve the current situation that research on sellers’ attributes has not received enough attention in the field of E-commerce study, this paper analyzes the E-commerce transaction data of cellular phones with Self-Organizing Map (SOM) and explores how the match between sellers’ attributes and the commodities affect the prosperity of the transactions. The findings will help sellers and manufacturers understand the market status and their own advantages and disadvantages, and they can take measures to improve their production and operation. In the aspect of research methods, the algorithm of U-matrix has been modified based on the existing version,then a new definition and display of U-matrix are proposed and applied to the data analysis in this paper.

Key wordsE-commerce      Self-Organizing Map      Neural Network     
Received: 22 May 2008      Published: 25 September 2008



Corresponding Authors: An Lu     E-mail:
About author:: Li Gang,An Lu

Cite this article:

Li Gang,An Lu. Clustering Analysis of E-commerce Transactions with Self-Organizing Map. New Technology of Library and Information Service, 2008, 24(9): 70-77.

URL:     OR

[1] 薛松,杨小丽.我国电子商务发展中的问题及对策分析[J].中国科教创新导刊,2007(20): 185-186.
[2] 中国互联网络发展状况统计报告[EB/OL].[2007-07-07].
[3] 张孟才,王新辉.基于BP神经网络的国际电子商务信用风险预警模型研究[J].集团经济研究,2007(11z):321.
[4] 田玲,支芬和,陈道志.基于模糊神经网络的客户分类方法研究[J].生产力研究,2007(12):120-121.
[5] 闵惜琳.人工神经网络结合遗传算法对网站开发优化的应用[J].系统工程,2007,25(2): 22-26.
[6] 吕晓玲,吴喜之.电子商务客户网络购物行为挖掘[J].统计与信息论坛,2007,22(3):29-32.
[7] Kohonen T. Self-Organized Formation of Topologically Correct Feature Maps[J]. Biological Cybernetics, 1982, 43(1):59-69.
[8] Kohonen T. Self-Organizing Maps [M]. 2nd ed.Berlin: Springer, 1997.
[9] 高琳琦,李从东.基于自组织特征映射聚类的协同过滤推荐算法[J].天津大学学报,2006,39(B6): 279-282.
[10] 包书哲,周东清,侯志刚.一个文本挖掘方法在扩展的电子商务系统中的应用[J].计算机应用研究,2003,20(12):107-108.
[11] Vellido A, Lisboa P J G, Meehan K. Segmentation of the On-line Shopping Market Using Neural Networks [J]. Expert Systems with Applications, 1999, 17(4): 303-314.
[12] Changchien S, Lu T. Mining Association Rules Procedure to Support On-line Recommendation by Customers and Products Fragmentation [J]. Expert Systems with Applications, 2001, 20(4): 325-335.
[13] 黄平,付淇,李孟伦.基于淘宝网的体育商品网络营销策略[J].商场现代化,2007(11X): 125-126.
[14] Ultsch A, Siemon HP. Kohonen’s Self-Organizing Feature Maps for Exploratory Data Analysis[C]. In: Proceedings of International Neural Network Conference (INNC’90), Kluwer, Dordrecht, the Netherland. 1990: 305-308.
[15] Kohonen T. Self-Organizing Maps[M]. 3rd ed.Berlin: Springer, 2001.
[16] About SOM Toolbox[EB/OL].[2005-03-18]
[17] Ding C, Patra J C. User Modeling for Personalized Web Search with Self-Organizing Map [J]. Journal of the American Society for Information Science and Technology, 2007, 58(4): 494-507.
[18] Ultsch A, Herrmann L. Architecture of Emergent Self-Organizing Maps to Reduce Projection Errors[C]. In: Proceedings of the 13th European Symposium on Artificial Neural Network,Bruges. 2005:1-6.
[19] Vesanto J, Himberg J, Alhoniemi E, etal. SOM Toolbox for Matlab 5[EB/OL]. (2000-04-20). [2007-09-20]
[20] Kowalski G. Information Retrieval Systems: Theory and Implementation[M]. Norwell: Kluwer Academic Publishers, 1997.
[21] 侍文叶.2007年12月最受关注15大手机品牌排行榜[EB/OL].[2008-01-02].

[1] Gu Yaowen, Zhang Bowen, Zheng Si, Yang Fengchun, Li Jiao. Predicting Drug ADMET Properties Based on Graph Attention Network[J]. 数据分析与知识发现, 2021, 5(8): 76-85.
[2] Zhang Le, Leng Jidong, Lv Xueqiang, Cui Zhuo, Wang Lei, You Xindong. RLCPAR: A Rewriting Model for Chinese Patent Abstracts Based on Reinforcement Learning[J]. 数据分析与知识发现, 2021, 5(7): 59-69.
[3] Han Pu,Zhang Zhanpeng,Zhang Mingtao,Gu Liang. Normalizing Chinese Disease Names with Multi-feature Fusion[J]. 数据分析与知识发现, 2021, 5(5): 83-94.
[4] Wang Nan,Li Hairong,Tan Shuru. Predicting of Public Opinion Reversal with Improved SMOTE Algorithm and Ensemble Learning[J]. 数据分析与知识发现, 2021, 5(4): 37-48.
[5] Li Danyang, Gan Mingxin. Music Recommendation Method Based on Multi-Source Information Fusion[J]. 数据分析与知识发现, 2021, 5(2): 94-105.
[6] Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[7] Yin Haoran,Cao Jinxuan,Cao Luzhe,Wang Guodong. Identifying Emergency Elements Based on BiGRU-AM Model with Extended Semantic Dimension[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
[8] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[9] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[10] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[11] Yan Chun,Liu Lu. Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
[12] Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[13] Xu Yuemei,Liu Yunwen,Cai Lianqiao. Predicitng Retweets of Government Microblogs with Deep-combined Features[J]. 数据分析与知识发现, 2020, 4(2/3): 18-28.
[14] Xiang Fei,Xie Yaotan. Recognition Model of Patient Reviews Based on Mixed Sampling and Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 39-47.
[15] Ni Weijian,Guo Haoyu,Liu Tong,Zeng Qingtian. Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks[J]. 数据分析与知识发现, 2020, 4(2/3): 68-77.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938