Because of the nature of conventional 0-1 part-family incidence matrix, a multi-cell flexible manufacturing systems (MCFMS) using conventional part-family formation algorithms, such as array-based clustering, similarity coefficient-based clustering, and mathematical programming, in a cellular manufacturing mode can assign a part family only to one machine cell. The consequence is that each part...

Symbolic Data Analysis is based on a special descriptions of data – symbolic objects. Such descriptions preserve more detailed information about data than the usual representations with mean values. A special kind of symbolic object is also representation with distributions. In the clustering process this representation enables us to consider the variables of all types at the same time. We pres...

The rapid progress in the field of information technology, especially internet, has given birth to a lot information. ease publishing an article on website causes explosion news pages which will certainly confuse readers. diversity and increasing number articles make it increasingly difficult for internet users find large piles data online newspaper sites Aceh. grouping text documents is needed...

Image segmentation plays a significant role in computer vision. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation. Clustering is a powerful technique that has been reached in image segmentation. The cluster analysis is to partition an image data set into a number of disjoint groups or clusters. The clustering meth...

Due to fast growth of the internet technology there is need to establish security mechanism. So for achieving this objective NIDS is used. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model generation is computation intensive, hence to redu...

This paper proposes a novel fuzzy neural network model based on fuzzy clustering method. The model can accept continuous and discrete inputs together; the discrete input to the model is divided into several clusters by using fuzzy c-mean clustering algorithm (FCM). A fuzzy clustering neuron (FC-neuron) is designed to calculate a membership degree value belonging to one cluster for each discrete...

This article introduces a method for objectively separating and validating forecast scenarios within a large multimodel ensemble for the medium-range (3–7 day) forecasts of extratropical cyclones impacting the U.S. East Coast. The method applies fuzzy clustering to the principal components (PCs) of empirical orthogonal function (EOF) analysis on mean sea level pressure (MSLP) from a 90-member c...

Data clustering is a combinatorial optimization problem. This article shows that clustering is also an optimization problem for an analytic function. The mean squared error, or in this case, the squared error can expressed as an analytic function. With an analytic function we benefit from the existence of standard optimization methods: the gradient of this function is calculated and the descent...

MOTIVATION Microarray experiments have revolutionized the study of gene expression with their ability to generate large amounts of data. This article describes an alternative to existing approaches to clustering of gene expression profiles; the key idea is to cluster in stages using a hierarchy of distance measures. This method is motivated by the way in which the human mind sorts and so groups...