This paper presents a new clustering algorithm ,which is based on fuzzy c-lines, can treat data with some errors. First, the tolerance is formulated and introduce into optimization problem of clustering. Next, the problem is solved using Karush-Kuhn-Tucker conditions. Last, the algorithm is constructed based on the results of solving the problem. Some numerical examples for the proposed method ...

The grade of membership (GoM) model uses fuzzy sets as memberships of each individual to extreme profiles (or classes) on the likelihood function of multivariate multinomial distributions. The GoM clustering algorithm derived from the GoM model is used in cluster analysis for categorical data, but it is iterated with complicated calculations. In this paper we create another approach, termed a f...

Some major problems in clustering are: i) find the optimal number K of clusters; ii) assess the validity of a given clustering; iii) permit the classes to form natural shapes rather than forcing them into normed balls of the distance function; iv) prevent the order in which the feature vectors are read in from affecting the clustering; and v) prevent the order of merging from affecting the clus...

This paper presents a short overview of methods for fuzzy clustering and states desired properties for an optimal fuzzy document clustering algorithm. Based on these criteria we chose one of the fuzzy clustering most prominent methods – the c-means, more precisely probabilistic c-means. This algorithm is presented in more detail along with some empirical results of the clustering of 2-dimension...

In this paper, we describe the application of a novel spatial fuzzy clustering algorithm to the lip segmentation problem. The proposed spatial fuzzy clustering algorithm is able to take into account both the distributions of data in feature space and the spatial interactions between neighboring pixels during clustering. By appropriate preand postprocessing utilizing the color and shape properti...

This paper proposes the exchange market algorithm (EMA) to solve the combined economic and emission dispatch (CEED) problems in thermal power plants. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In order to show the capabilities ...

Group technology (GT) is a useful way to increase productivity with high quality in flexible manufacturing systems. Cell formation (CF) is a key step in GT. It is used to design a good cellular manufacturing system that uses the similarity measure between parts and machines so that it can identify part families and machine groups. Recently, fuzzy clustering has been applied in GT because the fu...

Formal Concept Analysis (FCA) and concept lattices have shown their effectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision tre...

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 ...