clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. The term “clustering” is used in several research communities to describe methods for grouping of unlabeled data. These communities have different terminologies and assumptions for the components of the clustering process and the context in which clustering is used. This paper looks into th...

1 M.Tech Student 2 Assistant Professor 1,2 Department of Computer Science and Engineering 1,2 Shivalik Institute Of Engineering &Technology, Aliyaspur, Ambala Abstract— This research work examines the competing issues of energy consumption efficiency in wireless sensor networks. For this purpose, we considered a multi-objective Particle Swarm Optimization (PSO) in the selection of Cluster Head ...

Software systems evolve and change with time due to change in business needs with the result that at some stage, the original design and architecture descriptions may not give exact representation of the actual software system. Accurate understanding of software architecture is very important for software maintenance because it helps in estimating scope of change, re-usability, cost, and risk i...

LEACH is a traditional clustering routing protocol in wireless sensor network. When selecting the cluster head, Leach does not consider sensor nodes’ energy and position information. It leads to uneven the distribution of cluster heads and uneven energy consumption of sensor nodes. Clustering routing protocols which are based on particle swarm optimization have been proposed to improve the perf...

Text document clustering is one of the most widely studied data mining problems. It organizes text documents into groups such that each group has similar text documents. While grouping text documents, several issues have been observed. Accuracy and Efficiency are the main issues in text document clustering. Recently, as clustering problem can be mapped to optimization problem, evolutionary opti...

Affinity propagation (AP) is a clustering algorithm which has much better performance than traditional clustering approach such as K-means algorithm. AP can usually find a moderate clustering number, but “moderate” usually may not be the “optimal”. If we have found the optimal clustering number of AP, to estimate the input “preferences” (p) and the effective corresponding “preferences” (p) inte...

An image clustering method that is based on the particle swarm optimizer (PSO) is developed in this paper. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar image primitives. To illustrate its wide applicability, the proposed image classifier has been applied to synthetic, MRI and satellite images. Experimental results show that...

Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bio-inspired techniques. Particle swarm optimization (PSO...