Apr 25, 2016 · Moreover, this paper introduces a new approach for combining the evaluated individual clustering results without the procedure of thresholding.

Abstract—Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine ...

Accordingly, this paper develops a novel framework for clustering problems, which is called Weighted Spectral Cluster Ensemble (WSCE), by exploiting some ...

A novel framework for clustering problems is developed, which is called Weighted Spectral Cluster Ensemble (WSCE), by exploiting some concepts from ...

Accordingly, this paper develops a novel framework for clustering problems, which is called Weighted Spectral Cluster Ensemble (WSCE), by exploiting some ...

An intuitive idea of weighted clustering ensemble is to give a weight to each base clustering according to its quality/diversity in the clustering ensemble.

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable ...

According to the calculation rules of weight, WCE methods are mainly divided into fixed-weight and variable-weight methods [30]. Fix-weight methods [8, ...

Abstract. Ensemble clustering is a technique which combines multiple clustering results, and instance weighting is a technique which highlights.

We propose three different consensus techniques to leverage the weighted objects. All three reduce the ensemble clustering problem to a graph partitioning one.