Feature selection is an important step when building a classifier. However, the feature selection tends to be unstable on high-dimension and small-sample size data. This instability reduces the usefulness of selected features for knowledge discovery: if the selected feature subset is not robust, domain experts can have little trust that they are relevant. A growing number of studies deal with f...

Cluster ensembles aim to find better, more natural clusterings by combining multiple clusterings. We apply ensemble clustering to anomaly detection, hypothesizing that multiple views of the data will improve the detection of attacks. Each clustering rates how anomalous a point is; ratings are combined by averaging or taking either the minimum, the maximum, or median score. The evaluation shows ...

Data clustering is a challenging task in data mining technique. Various clustering algorithms are developed to cluster or categorize the datasets. Many algorithms are used to cluster the categorical data. Some algorithms cannot be directly applied for clustering of categorical data. Several attempts have been made to solve the problem of clustering categorical data via cluster ensembles. But th...

This paper investigates an ensemble feature selection algorithm that is based on genetic algorithms. The task of ensemble feature selection is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble’s classifiers...

Ensembles of classiiers have been shown to be very eeective for case-based classiication tasks. The vast majority of ensemble construction algorithms use the complete set of features available in the problem domain for the ensemble creation. Recent work on randomly selected subspaces for ensemble construction has been shown to improve the accuracy of the ensemble considerably. In this paper we ...

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, also referred to as consensus clustering, has emerged a method of combining an ensemble different clusterings derive final clustering that is better quality and robust than any single in the ensemble. Normally algorithms literature combine all without learning But by one can define merit or even cluster it, forming consensus. In this work, we propose cluster-level surprisal...

A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensmble framework is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard data sets. The...