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Clustering in the presence of side information: a non-linear approach

International Journal of Intelligent Computing and Cybernetics 2019年第2期12卷 292-314页

作者： Ahmad Ali Abin Faculty of Computer Science and Engineering Shahid Beheshti UniversityTehranIran

Purpose–Constrained clustering is an important recent development in clustering *** goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background *** purpose of this paper is to suggest a new perspective for constrained clustering,by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must-and cannot-link ***/methodology/approach–Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target *** previous efforts,the author presents a non-linear method for constraint clustering,whose basic idea is to use different non-linear functions for each dimension in target ***–The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linearfunctions for each dimension in target *** proposed method for a particular case is formulated and explained for quadratic *** reduce the number of optimization parameters,the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to *** results on synthetic and real-world data demonstrate the efficacy of the proposed ***/value–This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel *** work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.

关键词： Cannot-link Constrained clustering Instance-level constraints Must-link quadratic functions,side information

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