In this paper we propose a new evolutionary clustering algorithm named E-means. E-means is an Evolutionary extension of k-means algorithm that is composed by a revised k-means algorithm and an evolutionary approach to Gaussian mixture model, which estimates automatically the number of clusters and the optimal mean for each cluster. More specifically, the proposed Emeans algorithm defines an ent...

In this paper an improved hierarchical clustering algorithm by a P system with active membranes is proposed which provides new ideas and methods for cluster analysis. The membrane system has great parallelism. It could reduce the computational time complexity and is suitable for the clustering problem. Firstly an improved hierarchical algorithm was presented which introduced the K-medoids algor...

-In this paper targeted a variety of techniques, tactics and distinctive areas of the studies that are useful and marked because the crucial discipline of information mining technologies. The overall purpose of the system of statistics mining is to extract beneficial facts from a large set of information and changing it right into a shape that is comprehensible for in addition use. Clustering i...

Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotypin...

K-means Clustering is an important algorithm for identifying the structure in data. K-means is the simplest clustering algorithm [8]. This algorithm uses as input a predefined number of clusters i.e., the K from its name. Mean stands for an average, an average location of all the members of a particular cluster. In this work, a novel approach to seeding the clusters with a latent data structure...

In this article, the effective circuit partitioning techniques are employed by using the clustering algorithms. The technique uses the circuit netlist in order to cluster the circuit in partitioning steps and it also minimizes the interconnection distance with the required iteration level. The clustering algorithm like K-Mean, Y-Mean, K-Medoid are performed on the standard benchmark circuits. T...

Colors in an image provides tremendous amount of information. Using this color information images can be segmented, analyzed, labeled and indexed. In content based image retrieval system, color is one of the basic primitive features used. In Prevalent Color Extraction and indexing, the most extensive color on an image is identified and it is used for indexing. For implementation, Asteroideae fl...

The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and click streams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. D-Stream algorithm is an extended grid-based clustering algorithm for different dimen...