Computer Science ›› 2015, Vol. 42 ›› Issue (5): 57-61.doi: 10.11896/j.issn.1002-137X.2015.05.012

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Research on Clustering with Feature Preferences

FANG Ling and CHEN Song-can   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Traditional clustering methods,such as k-means and fuzzy c-means,do not generally distinguish different contributions or importance of data features to individual clusters,thus when facing high dimensional data,they often lead to lower clustering performance due to hardly considering the presence of high correlation or redundancy between features.In order to mitigate such adversity,with the introduction of the feature weights for each cluster in the clustering objective,we could automatically obtain not only the cluster-dependent weights but also the enhanced clustering performance.Though so,the feature weights obtained by an unsupervised clustering algorithm do not necessarily match the relative importance (or preferences) between the features as users expect.Thus this paper attempted to take advantage of actual preferences from users to design a clustering method which can reflect the feature preference.As a result,the proposed method not only extends the existing clustering methods with globally-weighted cluster-independent features to the one with locally-weighted cluster-dependent features but alos improves the clustering performance for feature preferences.

Key words: Clustering analysis,Feature preferences,Feature weighting,Cluster-dependent,Quadratic programming

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