Cluster ensembles generate a large number of different clustering solutions and combine them into a more robust and accurate consensus clustering. On forming the ensembles, the literature has suggested that higher diversity among ensemble members produces higher performance gain. In contrast, some studies also indicated that medium diversity leads to the best performing ensembles. Such contradi...

This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions to form a smaller but better performing cluster ensemble than using all available solutions. We design our ensemble selection methods based on quality and diversity, the two factors that have been shown to influence clu...

Clustering ensemble performance is affected by two main factors: diversity and quality. Selection of a subset of available ensemble members based on diversity and quality often leads to a more accurate ensemble solution. However, there is not a certain relationship between diversity and quality in selection of subset of ensemble members. This paper proposes the Hierarchical Cluster Ensemble Sel...

the wisdom of crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. this theory used for in clustering problems. previous researches showed that this theory can significantly increase the stability and performance of lea...

In this paper we proposed a method of optimal selection of cluster for cluster oriented classifier. The cluster oriented classifier is great advantage over binary and conventional classifier. The cluster oriented classifier work very efficiently on real and sample data. But the cluster oriented ensemble classifier faced a problem of selection of number of cluster for ensemble. In current fashio...

Fusion and ensemble is important technique of machine learning. Fusion fused the feature attribute of different classifier and improved the classification of binary classifier. Instead of that ensemble technique provide the facility of merge two individual classifier and improve the performance of both classifiers. The ensemble technique of classifier depends on number of nearer point of classi...

Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ensembles have been examined in the literature, e.g., random feature selection, weak clusterers, random projections, etc. Typically, one heuristic is picked at a time to construct the ensemble. To increase diversity of the ense...

Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation approaches measuring the efficacy of a set of p...

Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity measure is subsequently proposed. Although the measure was found to be related to the quality of the ensemble, this relationship appeared to be non-monotonic. In some cases, ensembles which exhibited a moderate level of diversity gave a more accurate clustering. Based on this, a procedure for building a cluste...