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When clustering incomplete datasets, data on cluster border (border data) are more likely to be misclassified. Aiming at this problem, the proposed algorithm focuses on the re-classification of “suspected misclassified” border data (abbreviated as SM border data). Based on the preliminary clustering results of classical FCM-based algorithm for incomplete data and the KNN (k nearest neighbor) principle,...
The fuzzy c-means algorithm is a useful technique for clustering real s-dimensional data, but it can not be directly used for partially missing data sets. In this paper, the problem of missing data handling for fuzzy clustering is considered, and a statistical representation of missing attributes is proposed. The approach reduces the statistical analysis of missing attributes to the subsets of the...
The ReliefF algorithm is an important attribute weighting approach, which is built on the basis of classification labels. And the attribute weights of weighted FCM (WFCM), a popular fuzzy clustering algorithm, can be gotten by ReliefF. In the light of the idea of collaborative learning, a collaborative optimization of clustering by fuzzy c-means and weight determination by ReliefF (Co-WFCM) is introduced...
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