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Feature weighted clustering for user profiling

International Journal of Modeling, Simulation, and Scientific Computing 2017年第4期8卷 245-261页

作者： Ayse Cufoglu Mahi Lohi colin everiss Department of Engineering University of Westminster 309 Regent StreetW1B 2HWLondonUK Department of Computer Science University of Westminster 309 Regent StreetW1B 2HWLondonUK

Personalization is the adaptation of the services to fit the user’s interests,characteristics and *** key to effective personalization is user *** from traditional collaborative and content-based approaches,a number of classification and clustering algorithms have been used to classify user related information to create user ***,they are not able to achieve accurate user *** this paper,we present a new clustering algorithm,namely Multi-Dimensional Clustering(MDC),to determine user *** MDC is a version of the Instance-Based Learner(IBL)algorithm that assigns weights to feature values and considers these weights for the *** feature weight methods are proposed for the MDC and,all three,have been tested and *** were conducted with using two sets of user profile datasets,which are the training(includes 10,000 instances)and test(includes 1000 instances)*** datasets reflect each user’s personal information,preferences and *** simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed *** results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other *** work is based on the doctoral thesis of the corresponding author.

关键词： Classification clustering mining methods and algorithms user profiling and personalization

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