Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

the capacitated clustering problem (ccp) is one of the most importantcombinational optimization problems that nowadays has many real applications inindustrial and service problems. in the ccp, a given n nodes with known demandsmust be partitioned into k distinct clusters in which each cluster is detailed by anode acting as a cluster center of this cluster. the objective is to minimize the sumof...

one of the most important aspects of software project management is the estimation of cost and time required for running information system. therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. software cost estimation refers to the process of development requirement prediction of software system. various kinds of effort estimation patter...

Abstract—Image segmentation is very important step of image analysis which is used to partitioned image into several homogenous regions by classifying pixels of whole image into different regions that exhibit similar characters. The result of image segmentation is a set of sections that together cover the whole image. This paper has presented a review on various image segmentations techniques l...

Natural Computing (NC) is a novel approach to solve real life problems inspired in the life itself. A diversity of algorithms had been proposed such as evolutionary techniques, Genetic Algorithms and Particle Swarm Optimization (PSO). These approaches, together with fuzzy and neural networks, give powerful tools for researchers in a diversity of problems of optimization, classification, data an...

Permeability, the ability of rocks to flow hydrocarbons, is directly determined from core. Due to high cost associated with coring, many techniques have been suggested to predict permeability from the easy-to-obtain and frequent properties of reservoirs such as log derived porosity. This study was carried out to put clustering methods (dynamic clustering (DC), ascending hierarchical clustering ...

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...

Along with increasingly development of CAD/CAM software and their application in various industries, minimizing of the machining time is found to be more important. In this paper, firstly the concerning subjects are discussed regarding classification of the optimization techniques. These are programming techniques, high speed machining techniques and feed rate optimization techniques. As a case...

Clustering problems appear in a wide range of unsupervised classification applications such as pattern recognition, vector quantization, data mining and knowledge discovery. The k-means algorithm is one of the most widely used clustering techniques. Unfortunately, k-means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is qui...

Data mining is a collection of techniques used to extract useful information from large data bases. Data clustering is a popular data mining technique. It is the task of grouping a set of objects into classes such that similar objects are placed in the same cluster while dissimilar objects are in separate clusters. Fuzzy cmeans (FCM) is one of the most popular clustering algorithms. However, it...