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Aiming at the information overload caused by rich resources of Broadcast Television programs, this paper puts forward Broadcast Television Programs Recommendation Technology based on user clustering. According to user rating data and programs broadcasting data, we cluster users by the improved K-MEANS algorithm, divide the users with similar viewing preference into the same community groups, and generate...
Aiming to meet the demand of intellectual delivery business, this paper puts forward Broadcast Television user dividing groups technology based on concept data clustering ensemble. Firstly, by Glass data, Blance data and Zoo data in UCI, the paper verify that concept data clustering ensemble technique based on K-MODES method can get more stable and more accurate results than K-MEANS method. Secondly,...
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