Applying cluster-based fuzzy association rules mining framework into EC environment |
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| Authors: | Hung-Pin Chiu Yi-Tsung Tang Kun-Lin Hsieh |
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| Affiliation: | 1. Department of Information Management, Nan Hua University, No. 32, Chung Keng Li, Dalin, Chiayi 622, Taiwan, ROC;2. Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC;3. Department of Information Management, National Taitung University, No. 684, Chung Hua Rd., Sec. 1, Taitung, Taiwan, ROC;1. Department of Computer Engineering, Wroc?aw University of Science and Technology, Wybrze?e Wyspiańskiego 27, 50-370 Wroc?aw, Poland;2. Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montréal, Québec, Canada H3G 1M8;3. Department of Pathology and Oncological Cytology, Medical University of Wroc?aw, Borowska 213, 50-556 Wroc?aw, Poland;2. Division of Vascular Surgery, VCU Health System, Richmond, Virginia;1. Business School, University of Shanghai for Science and Technology, Shanghai, China;2. Department of Computer Science, University of Jaen, Jaen, Spain;3. Facultad de Economia y Empresa, BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, Salamanca, Spain;4. School of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, China;1. Department of Mathematics, Faculty of Physical Science, Ahmadu Bello University, Zaria, Nigeria;2. Department of Mathematics, Faculty of Physical Science, Federal University Lokoja, Nigeria;1. Paris School of Economics, Université Paris I - Panthéon-Sorbonne, Paris, France;2. Thales Research & Technology, Palaiseau, France;3. SINCLAIR AI Lab, Palaiseau, France |
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| Abstract: | During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach. |
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