Ensemble P-spectral Semi-supervised Clustering

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 p-spectral 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.