In presence of multiple clustering solutions for the same dataset, a clustering ensemble approach aims to yield a single clustering of the dataset by achieving a consensus among the input clustering solutions. The goal of this consensus is to improve the quality of clustering. It has been seen that there are some image clustering tasks that cannot be easily solved by computer. But if these imag...

Clustering has been an important tool for extracting underlying gene expression patterns from massive microarray data. However, most of the existing clustering methods cannot automatically separate noise genes, including scattered, singleton and mini-cluster genes, from other genes. Inclusion of noise genes into regular clustering processes can impede identification of gene expression patterns....

Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better t...

To learn any problem, many classifiers have been introduced so far. Each of these classifiers has many strengths (positive aspects) and weaknesses (negative aspects) that make it suitable for some specific problems. But there is no powerful solution to indicate which classifier is the best classifier (or at least a good one) for a special problem. Fortunately the ensemble learning provides us w...

In this paper, we propose a new ensemble clustering approach termed ensemble clustering using factor graph (ECFG). Compared to the existing approaches, our approach has three main advantages: (1) the cluster number is obtained automatically and need not to be specified in advance; (2) the reliability of each base clustering can be estimated in an unsupervised manner and exploited in the consens...