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 ...

It is known that an ensemble of classifiers can outperform a single best classifier if classifiers in the ensemble are sufficiently diverse (i.e., their errors are as much uncorrelated as possible) and accurate. We study ensembles of nearest neighbours for cancer classification based on gene expression data. Such ensembles have been rarely used, because the traditional ensemble methods such as ...

The correlation function of galaxy clusters has frequently been used as a test of cosmological models. A number of assumptions are implicit in the comparison of theoretical expectations to data. Here we use an ensemble of ten large N-body simulations of the standard cold dark matter cosmology to investigate how cluster selection criteria and other uncertain factors influence the cluster correla...

In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model de ̄nes a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. De...

Since accurate classification of DNA microarray is a very important issue for the treatment of cancer, it is more desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of the many advantages of mutually error-correlated ensemble classifiers, they are limited in performance. It is difficult to c...

A novel method of introducing diversity into ensemble learning predictors for regression problems is presented. The proposed method prunes the ensemble while simultaneously training, as part of the same learning process. Here not all members of the ensemble are trained, but selectively trained, resulting in a diverse selection of ensemble members that have strengths in different parts of the tr...

Ensemble learning has been widely applied to both batch data classification and streaming classification. For the latter setting, most existing ensemble systems are homogenous, which means they generated from only one type of model. In contrast, by combining several types different models, a heterogeneous system can achieve greater diversity among its members, helps improve performance. Althoug...

In this paper, we describe the implementation of an unsupervised learning method for Chinese word sense induction in CIPS-SIGHAN-2010 bakeoff. We present three individual clustering algorithms and the ensemble of them, and discuss in particular different approaches to represent text and select features. Our main system based on cluster ensemble achieves 79.33% in F-score, the best result of thi...