The 11th Iranian and the first International Conference on Machine Vision and Image Processing

Ensemble P-spectral Semi-supervisedClustering

Sedigheh Safari, Fatemeh Afsari
The 11th Iranian and the first International Conference on Machine Vision and Image Processing (MVIP 2020)

Abstract

This paper proposes an ensemble p-spectral semi-supervised clustering algorithm for very high dimensional data sets. Traditional clustering and semi-supervised clustering approaches have several shortcomings; do not use the prior knowledge of experts and researchers; not good for high dimensional data; and use less constraint pairs. To overcome, we first apply the transitive closure operator to the pairwise constraints. Then the whole feature space is divided into several subspaces to find the ensemble semi-supervised pspectral clustering of the whole data. Also, we search to find the best subspace by using three operators. Experiments show that the proposed ensemble pspectral clustering method outperforms the existing semi-supervised clustering methods on several high dimensional data sets.

Keywords: clustering, Semi-supervised, Ensemble Learning, Subspace Learning, Pairwise Constraints



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