We develop a consensus clustering framework proposed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods. keywords: consensus clustering, ensemble clustering, least squares

An extensive amount of work has been done in data clustering research under the unsupervised learning technique in Data Mining during the past two decades. Moreover, several approaches and methods have been emerged focusing on clustering diverse data types, features of cluster models and similarity rates of clusters. However, none of the single clustering algorithm exemplifies its best nature i...

Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles me...

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...

It is difficult from possibilities to select a most suitable effective way of clustering algorithm and its dataset, for a defined set of gene expression data, because we have a huge number of ways and huge number of gene expressions. At present many researchers are preferring to use hierarchical clustering in different forms, this is no more totally optimal. Cluster ensemble research can solve ...

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high...

Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. ...

Though many cluster ensemble approaches came forward as a potential and dominant method for enhancing the robustness, stability and the quality of individual clustering systems, it is intensely observed that this approach in most cases generate a final data partition with deficient information. The primary ensemble information matrix generated in the traditional cluster ensemble approaches resu...

Consensus clustering methodologies combine a set of partitions on the clustering ensemble providing a consensus partition. One of the drawbacks of the standard combination algorithms is that all the partitions of the ensemble have the same weight on the aggregation process. By making a differentiation among the partitions the quality of the consensus could be improved. In this paper we propose ...

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...