OBJECTIVE Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the exploration of new strategies and in the...

In this paper, it is proposed that coastal flood ensemble forecasts be partitioned into sub-ensemble forecasts using cluster analysis in order to produce representative statistics and to measure forecast uncertainty arising from the presence of clusters. After clustering the ensemble members, the ability to predict the cluster into which the observation will fall can be measured using a cluster...

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...

Detecting protein complexes from protein-protein interaction (PPI) networks is a challenging task in computational biology. A vast number of computational methods have been proposed to undertake this task. However, each computational method is developed to capture one aspect of the network. The performance of different methods on the same network can differ substantially, even the same method m...

Clustering ensemble has emerged as an important extension of the classical clustering problem. It provides a framework for combining multiple base clusterings of a data set to generate a final consensus result. Most existing clustering methods simply combine clustering results without taking into account the noises, which may degrade the clustering performance. In this paper, we propose a novel...

We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, documentconcept and document-category. A final clustering solution is obtained by exploiting associations between document pairs an...

Though subspace clustering, ensemble clustering, alternative clustering, and multiview clustering are different approaches motivated by different problems and aiming at different goals, there are similar problems in these fields. Here we shortly survey these areas from the point of view of subspace clustering. Based on this survey, we try to identify problems where the different research areas ...

This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identi...

Clustering is an important tool for data exploration. Several clustering algorithms exist, and new algorithms are frequently proposed in the literature. These algorithms have been very successful in a large number of real-world problems. However, there is no clustering algorithm, optimizing only a single criterion, able to reveal all types of structures (homogeneous or heterogeneous) present in...

Recent work has focused the incorporation of a priori knowledge into the data clustering process, in the form of pairwise constraints, aiming to improve clustering quality and find appropriate clustering solutions to specific tasks or interests. In this work, we integrate must-link and cannot-link constraints into the cluster ensemble framework. Two algorithms for combining multiple data partit...