Segmentation, or partitioning images into internally homogeneous regions, is an important first step in many Computer Vision tasks. In this paper, we attack the segmentation problem using an ensemble of low cost image segmentations. These segmentations are reconciled by applying recent techniques from the consensus clustering literature which exploit a Non-negative Matrix Factorization (NMF) fr...

Many clustering methods, such as K-means, kernel K-means, and MNcut clustering, follow the same recipe: (1) choose a measure of similarity between observations; (ii) define a figure of merit assigning a large value to partitions of the data that put similar observations in the same cluster; (iii) optimize this figure of merit over partitions. Potts model clustering, introduced by Blatt, Wiseman...

With the exploding volume of microarray experiments comes increasing interest in mining repositories of such data. Meaningfully combining results from varied experiments on an equal basis is a challenging task. Here we propose a general method for integrating heterogeneous data sets based on the consensus clustering formalism. Our method analyzes source-specific clusterings and identifies a con...

A novel framework for consensus clustering is presented which has the ability to determine both the number of clusters and a final solution using multiple algorithms. A consensus similarity matrix is formed from an ensemble using multiple algorithms and several values for k. A variety of dimension reduction techniques and clustering algorithms are considered for analysis. For noisy or high-dime...

The advances in next-generation sequencing technologies allow researchers to sequence in parallel millions of microbial organisms directly from environmental samples. The result of this “shotgun” sequencing are many short DNA fragments of different organisms, which constitute the basis for the field of metagenomics. Although there are big databases with known microbial DNA that allow us classif...

We consider the problem of Consensus Clustering. Given a finite set of input clusterings over some data items, a consensus clustering is a partitioning of the items which matches as closely as possible the given input clusterings. The best exact approach to tackling this problem is by modelling it as a Boolean Integer Program (BIP). Unfortunately, the size of the BIP grows cubically in the numb...

0

Unsupervised multirelational learning (clustering) in non-sparse domains such as molecular biology is especially difficult as most clustering algorithms tend to produce distinct clusters in slightly different runs (either with different initializations or with slightly different training data). In this paper we develop a {\em multirelational consensus clustering\/} algorithm based on nonnegativ...

In this paper, we consider that agents judge the feasible alternatives through linguistic terms –when they are confident in their opinions– or linguistic expressions formed by several consecutive linguistic terms –when they hesitate. In this context, we propose an agglomerative hierarchical clustering process where the clusters of agents are generated by using a distance-based consensus measure.

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable in...