This paper presents an approach for assessing cluster validity based on similarity knowledge extracted from the Gene Ontology (GO) and databases annotated to the GO. A knowledge-driven cluster validity assessment system for microarray data was implemented. Different methods were applied to measure similarity between yeast genes products based on the GO. This research proposes two methods for ca...

Variable string length genetic algorithm (GA) is used for developing a novel nonparametric clustering technique when the number of clusters is not fixed a priori. Chromosomes in the same population may now have different lengths since they encode different number of clusters. The crossover operator is redefined to tackle the concept of variable string length. Cluster validity index is used as a...

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selec...

introduction: now a day, resulting from life style modification and alteration, fatigue, as a consequence, is not uncommon symptom. diverse tools are available to measure fatigue status. one of which is facit fatigue scale. the facit is one of the most widely used questionnaires for screening fatigue. the questionnaire has been translated and validated? into 45 different languages, but there is...

Abstract Clustering can be defined as the process of grouping physical or abstract objects into classes of similar objects. It’s an unsupervised learning problem of organizing unlabeled objects into natural groups in such a way objects in the same group is more similar than objects in the different groups. Conventional clustering algorithms cannot handle uncertainty that exists in the real life...

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, medical image segmentation is complex, and automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, we present automatic fuzzy k-means, and kernelized fuzzy c-means algorithms by conside...

in this study, we considered some competitive learning methods which include hard competitive learning (hcl) and soft competitive learning (scl) with/ without fixed network dimensionality for reliability analysis in microarrays. in order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of func...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...