Given a simple undirected weighted or unweighted graph, we try to cluster the vertex set into communities and also to quantify the robustness of these clusters. For that task, we propose a new method, called bootstrap clustering which consists in (i) defining a new clustering algorithm for graphs, (ii) building a set of graphs similar to the initial one, (iii) applying the clustering method to ...

Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing information over temporal data. In this paper, we present a temporal data clustering framework via a weighted clustering produced by initial clustering analysis on different temporal data representations. In the existing system a novel weighted function guided by clustering validatio...

Traditional clustering ensemble methods combine all obtained clustering results at hand. However, we can often achieve a better clustering solution if only parts of the clustering results available are combined. In this paper, we generalize the selective clustering ensemble algorithm proposed by Azimi and Fern and a novel clustering ensemble method, SELective Spectral Clustering Ensemble (SELSC...

Article history: Available online 30 August 2011

In this paper we propose a clustering process which uses a multi-objective evolution to select a set of diverse clusterings. The selected clusterings are then combined using a consensus method. This approach is compared to a clustering process where no selection is applied. We show that careful selection of input ensemble members can improve the overall quality of the final clustering. Our algo...

Clustering ensemble mainly relies on the pairwise similarity to capture the consensus function. However, it usually considers each base clustering independently, and treats the similarity measure roughly with either 0 or 1. To address these two issues, we propose a coupled framework of clustering ensembles CCE, and exemplify it with the coupled version CCSPA for CSPA. Experiments demonstrate th...

This paper introduces a novel distance measure for clustering high dimensional data based on the hitting time of two Minimal Spanning Trees (MST) grown sequentially from a pair of points by Prim’s algorithm. When the proposed measure is used in conjunction with spectral clustering, we obtain a powerful clustering algorithm that is able to separate neighboring non-convex shaped clusters and to a...

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral for single multiple views been common solution. Despite its good performance, it a major limitation: requires an extra step clustering. This step, which could be famous k-means clustering, depends heavily on initialization, may affect quality result. To overcome this p...

Ensemble clustering is a promising approach that combines the results of multiple clustering algorithms to obtain a consensus partition by merging different partitions based upon well-defined rules. In this study, we use an ensemble clustering approach for merging the results of five different clustering algorithms that are sometimes used in bioinformatics applications. The ensemble clustering ...

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. A clustering ensmble framework based on the new metric is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. After extracting a large number of clusters some of them are selec...