Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Parti...

Clustering is the process to present the data in an effective and organized way. There are number of existing clustering approaches but most of them suffer with problem of data distribution. If the distribution is non linear it gives impurities in clustering process. The propose work is about to improve the accuracy of such clustering algorithm. Here we are presenting the work on time series or...

In this article we propose an integrative clustering approach for analysis of gene expression data across multiple experiments, based on Particle Swarm Optimization (PSO) and Formal Concept Analysis (FCA). In the proposed algorithm, the available microarray experiments are initially divided into groups of related datasets with respect to a predefined criterion. Subsequently, a hybrid clustering...

Data mining is the process of extracting previously unknown and valid information from large databases. Clustering is an important data analysis and data mining method. It is the unsupervised classification of objects into clusters such that the objects from same cluster are similar and objects from different clusters are dissimilar. Data clustering is a difficult unsupervised learning problem ...

Particle swarm optimization (PSO) is a meta-heuristic that has been found to be very successful in a wide variety of optimization tasks. The behavior of any meta-heuristic for a given problem is directed by both: the variation operators, and the values selected for the parameters of the algorithm. Therefore, it is only natural to expect that not only the parameters, but also the neighborhood to...