  Clustering explores meaningful patterns in the non-labeled data sets. Cluster
Ensemble Selection (CES) is a new approach, which can combine individual
clustering results for increasing the performance of the final results.
Although CES can achieve better final results in comparison with individual
clustering algorithms and cluster ensemble methods, its performance can be
dramatically affected by its consensus diversity metric and thresholding
procedure. There are two problems in CES: 1) most of the diversity metrics is
based on heuristic Shannon's entropy and 2) estimating threshold values are
really hard in practice. The main goal of this paper is proposing a robust
approach for solving the above mentioned problems. Accordingly, this paper
develops a novel framework for clustering problems, which is called Weighted
Spectral Cluster Ensemble (WSCE), by exploiting some concepts from community
detection arena and graph based clustering. Under this framework, a new version
of spectral clustering, which is called Two Kernels Spectral Clustering, is
used for generating graphs based individual clustering results. Further, by
using modularity, which is a famous metric in the community detection, on the
transformed graph representation of individual clustering results, our approach
provides an effective diversity estimation for individual clustering results.
Moreover, this paper introduces a new approach for combining the evaluated
individual clustering results without the procedure of thresholding.
Experimental study on varied data sets demonstrates that the prosed approach
achieves superior performance to state-of-the-art methods.
