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...

In order to have clarity in the satellite images we have used Particle Swarm Optimization technique. When incorporated with traditional clustering algorithms, problems such as local optima and sensitivity to initialization, are reduced, thus exploring a greater area using global search. This segmented image is further classified using Kappa coefficient. Keywords— Particle Swarm Optimization(PSO...

Particle swarm optimization is a based-population heuristic global optimization technology and is referred to as a swarm-intelligence technique. In general, each particle is initialized randomly which increases the iteration time and makes the result unstable. In this paper an improved clustering algorithm combined with entropy-based fuzzy clustering (EFC) is presented. Firstly EFC algorithm ge...

The goal of this article is to introduce two existing clustering approaches into the domain of ubiquitous knowledge discovery. First we demonstrate how horizontal collaborative clustering can be performed in a ubiquitous environment and discuss the ability of these two clustering techniques to cope with privacy constraints. Next, we illustrate how a particle swarm optimization driven version of...

By use of semantic attributes of 3D object, the user can search for targeted objects, which main advantage is that it does not require the user to sketch a 3D object as the query for 3D object retrieval, and the retrieval system can obtain a better retrieval performance. There are many categorical datum among these attributes, and how to use those and find the most similar objects is a vital pr...

In recent years, different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimization (PSO) algorithm. The main modification that this work made to the original PSO algorithm consists in not using the vector of velocities that the standard PSO algorithm does. The proposed algori...

Data mining plays a very important role in the analysis of diseases and clustering approach makes it easier to classify the data collected in respective groups. Medicine companies and medical appliance manufacturer are benefitted from these data analysis. Now a days, this is done at a very large scale and has been named as big data analysis in which data size is of many terabytes. Optimization ...

This paper presents a hybrid unsupervised clustering algorithm, referred to as the Rough Fuzzy C-Means (RFCM) algorithm and Particle Swarm Optimization (PSO). The PSO algorithm features high quality of searching in the nearoptimum. At the same time, in RFCM, the concept of lower and upper approximation can deal with uncertainty, vagueness and indiscernibility in cluster relations while the memb...

The Particle Swarm Optimization (PSO) algorithm, like many optimization algorithms, is designed to find a single optimal solution. When dealing with multimodal functions, it needs some modifications to be able to locate multiple optima. In a parallel with Evolutionary Computation algorithms, these modifications can be grouped in the framework of Niching. In this thesis, we present a new approac...

Clustering is a method which divides data objects into groups based on the information found in data that describes the objects and relationships among them. There are a variety of algorithms have been developed in recent years for solving problems of data clustering. Data clustering algorithms can be either hierarchical or partitioned. Most promising among them are K-means algorithm which is p...