To improve the performance of clustering ensemble method, a selective fuzzy clustering ensemble algorithm is proposed. It mainly includes selection of clustering ensemble members and combination of clustering results. In the process of member selection, measure method is defined to select the better clustering members. Then some selected clustering members are viewed as hyper-graph in order to ...

Clustering-ensemble methods have emerged recently as an effective approach to the problem of clustering, which is one of the fundamental data-analysis tools. Data clustering with an ensemble involves two steps: generation of the ensemble with single-clustering methods and the combination of the obtained solutions to produce a final consensus partition of the data. In this paper we first propose...

Ensemble clustering is a novel research field that extends to unsupervised learning the approach originally developed for classification and supervised learning problems. In particular ensemble clustering methods have been developed to improve the robustness and accuracy of clustering algorithms, as well as the ability to capture the structure of complex data. In many clustering applications an...

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...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with th...

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 ...

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving both the robustness and stability of results from individual clustering methods. Weighted ensemble arises naturally ensemble. One arguments weighted is that elements (clusterings clusters) in are different quality, objects features varying significance. However, it not possible to directly apply weighting...

A very promising approach to reach a robust partitioning is to use ensemble-based learning. In this way, the classification/clustering task is more reliable, because the classifiers/clusterers in the ensemble cover the faults of each other. The common policy in clustering ensemble based learning is to generate a set of primary partitionings that are different from each other. These primary part...