Credal c-means clustering method based on belief functions

Zhun Ga Liu, Quan Pan, Jean Dezert, Grégoire Mercier

科研成果: 期刊稿件文章同行评审

137 引用 (Scopus)

摘要

The recent credal partition approach allows the objects to belong to not only the singleton clusters but also the sets of clusters (i.e. meta-clusters) with different masses of belief. A new credal c-means (CCM) clustering method working with credal partition has been proposed in this work to effectively deal with the uncertain and imprecise data. In the clustering problem, one object simultaneously close to several clusters can be difficult to correctly classify, since these close clusters appear not very distinguishable for this object. In such case, the object will be cautiously committed by CCM to a meta-cluster (i.e. the disjunction of these close clusters), which can be considered as a transition cluster among these different close clusters. It can well characterize the imprecision of the class of the object and can also reduce the misclassification errors thanks to the use of meta-cluster. CCM is robust to the noisy data because of the outlier cluster. The clustering centers and the mass of belief on each cluster for any object are obtained by the optimization of a proper objective function in CCM. The effectiveness of CCM has been demonstrated by three experiments using synthetic and real data sets with respect to fuzzy c-means (FCM) and evidential c-means (ECM) clustering methods.

源语言英语
页(从-至)119-132
页数14
期刊Knowledge-Based Systems
74
DOI
出版状态已出版 - 2015

指纹

探究 'Credal c-means clustering method based on belief functions' 的科研主题。它们共同构成独一无二的指纹。

引用此