Abstract :
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
Keywords :
XML; data mining; electronic commerce; fuzzy set theory; mobile computing; pattern classification; pattern clustering; user modelling; XML based on fuzzy clustering; XML data clustering; XML data preprocessing; data classification; data mining; e-commerce; mobile commerce; personalized user profile; recommendation system; Business; Clustering algorithms; Clustering methods; Content management; Data engineering; Fuzzy systems; Information filtering; Information filters; Portals; XML;
