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...

Background: The new phenomenon of Internet addiction among teenagers and young adults is one of the modern addictions in industrial and post-industrial societies. The purpose of this research was to predict the Internet addiction based on the personality characteristics of high school students in Kerman. Methods: This research was a descriptive correlational study. The statistical population in...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

A K-means clustering approach is proposed for the automated diagnosis of defective rolling element bearings. Since K-means clustering is an unsupervised learning procedure, the method can be directly implemented to measured vibration data. Thus, the need for training the method with data measured on the specific machine under defective bearing conditions is eliminated. This fact consists the ma...

in this paper an application of gustafson-kessel clustering algorithm is presented to create a fault detection map (fdm). five post-stack seismic attributes are extracted from a desired seismic time slice related to 3d seismic data of a gas field located in southwest of iran. to find the optimal cluster numbers, two frequently used clustering validity measures, i.e. sc and xb, are used and then...

Single pass sequences of mRNA, called ESTs, have been determined extensively. They have been accumulated in the dbEST database in GenBank. The number of ESTs in dbEST has become more than eight million in August 2002. By clustering and assembling ESTs, we can conduct the following analyses. First, we can obtain complete ORF sequences based on ESTs that are fragment sequences of mRNA and do not ...

The paper analyses the impact of geographic innovation on Total Factor Productivity (TFP) in Taiwan in 2001 using 242 four-digit standard industrial classification (SIC) industries. We compute TFP by estimating Translog production functions with K, L, E and M inputs, and measure the geographic innovative activity using both Krugman's Gini coefficients and the location Herfindahl index. We also ...

Our research in on-line monitoring of industrial milling tools has focused on the occurrence of certain wide-band transient events. Time-frequency representations of these events appear to reveal a variety of classes of transients, and a time-structure to these classes which would be well modeled using hidden Markov models. However, the identities of these classes are not known, and obtaining a...

Sales forecasting is one of the most crucial issues addressed in business. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. this research propose an intelligent hybrid sales forecasting system Delphi-FCBPN sales forecast based on Delphi Method, fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learni...