نتایج جستجو برای: cluster ensemble selection

تعداد نتایج: 549829  

Journal: :Bioinformatics 2005
Chi Yu Chan Charles E. Lawrence Ye Ding

UNLABELLED The energy landscape of RNA secondary structures is often complex, and the Boltzmann-weighted ensemble usually contains distinct clusters. Furthermore, the minimum free energy structure often lies outside of the cluster containing the structure determined by comparative sequence analysis. We have developed procedures to characterize and visualize the Boltzmann-weighted ensemble, and ...

2014
Albert H.R. Ko Robert Sabourin Luiz E. S. Oliveira

The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important properties involved in the selection of the best EoC from a pool of classifiers is considered to be classifier diversity. In general, classifier diversity does not occur randomly, but is generated systematically by various ...

Journal: :international journal of smart electrical engineering 2013
mohsen jahanshahi shaban rahmani shaghayegh ghaderi

an efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. the implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. according to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses fuzzy inference systems to select cluster head. the alg...

Journal: :Bioinformatics 2010
Natthakan Iam-on Tossapon Boongoen Simon M. Garrett

MOTIVATION It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically comb...

Journal: :Pattern Recognition 2010
Sandro Vega-Pons Jyrko Correa-Morris José Ruiz-Shulcloper

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...

Journal: :Pattern Recognition 2015
Caiming Zhong Xiaodong Yue Zehua Zhang Jingsheng Lei

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable and accurate partition. One of the key problems of clustering ensemble is how to exploit the cluster structure information in each base partition. Evidence accumulation is an effective framework which can convert the base partitions into a co-association matrix. This matrix describes the frequency of a p...

Journal: :CoRR 2014
Albert Hung-Ren Ko Robert Sabourin Alceu S. Britto Luiz Eduardo Soares de Oliveira

The Ensemble of Classifiers (EoC) has been shown to be effective in improving the performance of single classifiers by combining their outputs, and one of the most important properties involved in the selection of the best EoC from a pool of classifiers is considered to be classifier diversity. In general, classifier diversity does not occur randomly, but is generated systematically by various ...

Journal: :The Journal of chemical physics 2009
R Stephen Berry Boris M Smirnov

We analyze the configurational excitation of a cluster for both a microcanonical and a canonical ensemble of atoms and apply this analysis to the Lennard-Jones cluster of 13 atoms. Dividing the cluster excitations into configurational and thermal classes, we evaluate the anharmonicity coefficient of atomic vibrations and the entropy jump as a function of temperature on the basis of computer sim...

2016
DEWAN MD. FARID ANN NOWE BERNARD MANDERICK

Clustering of high-dimensional biological big data is incredibly difficult and challenging task, as the data space is often too big and too messy. The conventional clustering methods can be inefficient and ineffective on high-dimensional biological big data, because traditional distance measures may be dominated by the noise in many dimensions. An additional challenge in biological big data is ...

2014
Kehan Gao Taghi M. Khoshgoftaar Randall Wald

High dimensionality is a major problem that affects the quality of training datasets and therefore classification models. Feature selection is frequently used to deal with this problem. The goal of feature selection is to choose the most relevant and important attributes from the raw dataset. Another major challenge to building effective classification models from binary datasets is class imbal...

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