TY - GEN
T1 - XFC - XML based on fuzzy clustering - Method for personalized user profile based on recommendation system
AU - Kim, Jin Hong
AU - Lee, Eun Seok
PY - 2004
Y1 - 2004
N2 - In data mining, to access a large amount of data sets for the purpose of predictive data does not guarantee a good method. Even, the size of Real data is unlimited in Mobile commerce. Hereupon, in addition to searching expected Products for Users, it becomes necessary to develop a recommendation service based on XML Technology. In this paper, we design the optimized XML Recommended products data. Efficient XML data preprocessing is required in include of formatting, structural, attribute of representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products and E-Commerce from M-Commerce to XDB. First, analyzing user profiles information. In the result creating clusters with user profile analyzed such as with set of sex, age, job. Second, it is clustering XML data, which are associative objects, classified from user profile in shopping mall. Third, after composing categories and Products in which associative Products exist from the first clustering, it represent categories and Products in shopping mall and optimized clustering XML data which are personalized products. The proposed personalizing user profile clustering method is designed and simulated to demonstrate the efficiency of the system.
AB - In data mining, to access a large amount of data sets for the purpose of predictive data does not guarantee a good method. Even, the size of Real data is unlimited in Mobile commerce. Hereupon, in addition to searching expected Products for Users, it becomes necessary to develop a recommendation service based on XML Technology. In this paper, we design the optimized XML Recommended products data. Efficient XML data preprocessing is required in include of formatting, structural, attribute of representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products and E-Commerce from M-Commerce to XDB. First, analyzing user profiles information. In the result creating clusters with user profile analyzed such as with set of sex, age, job. Second, it is clustering XML data, which are associative objects, classified from user profile in shopping mall. Third, after composing categories and Products in which associative Products exist from the first clustering, it represent categories and Products in shopping mall and optimized clustering XML data which are personalized products. The proposed personalizing user profile clustering method is designed and simulated to demonstrate the efficiency of the system.
UR - https://www.scopus.com/pages/publications/11244341390
M3 - Conference contribution
AN - SCOPUS:11244341390
SN - 0780386442
SN - 9780780386440
T3 - 2004 IEEE Conference on Cybernetics and Intelligent Systems
SP - 1201
EP - 1205
BT - 2004 IEEE Conference on Cybernetics and Intelligent Systems
T2 - 2004 IEEE Conference on Cybernetics and Intelligent Systems
Y2 - 1 December 2004 through 3 December 2004
ER -