In this article, a recent metaheuristic method, cat swarm optimization, is introduced to find the proper clustering of data sets. Two clustering approaches based on cat swarm optimization called Cat Swarm Optimization Clustering (CSOC) and K-harmonic means Cat Swarm Optimization Clustering (KCSOC) are proposed. In the proposed methods, seeking mode and tracing mode are adopted to exploit and ex...

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access electricity 2030. Efficient planning of distribution networks a complex multivariate task, which usually split into multiple subproblems reduce number variables. present work addresses problem optimal secondary substation siting, means different clustering...

The premise of this paper is to scroll the various search engine optimization techniques which are used in solving problems associated while finding relevant information related to specific topic across the Internet. While carrying this review work the authors concentrated on various published research papers and finally concluded that Query Clustering, Sequential Pattern Mining and Page Rankin...

This paper presents an ant colony optimization methodology for optimally clustering N objects into K clusters. The algorithm employs distributed agents which. AbstractAnt-based clustering is a biologically inspired data. Multi-ant colonies approach for clustering data that consists of some parallel.based on ant colony to solve the unsupervised clustering. Index TermsAnt colony optimization, Clu...

Clustering is a class of machine learning algorithms which has important applications in many different fields. Users often use clustering to find hidden structures from data for those domain specific problems. However, evaluating clustering results is always a hard problem. In many and perhaps most of these applications, users need to trade off competing goals and encode prior knowledge into t...

Clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for cluster...

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...