In this article we propose an integrative clustering approach for analysis of gene expression data across multiple experiments, based on Particle Swarm Optimization (PSO) and Formal Concept Analysis (FCA). In the proposed algorithm, the available microarray experiments are initially divided into groups of related datasets with respect to a predefined criterion. Subsequently, a hybrid clustering...

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables rep...

this paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. the mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. furthermore, ...

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables rep...

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we ha...

In this paper, we describe fuzzy agglomerative clustering, a brand new fuzzy clustering algorithm. The basic idea of the proposed algorithm is based on the well-known hierarchical clustering methods. To achieve the soft or fuzzy output of the hierarchical clustering, we combine the single-linkage and completelinkage strategy together with a fuzzy distance. As the algorithm was created recently,...

Keywords: Cluster analysis Fuzzy clustering Fuzzy c-means (FCM) Initialization Bias correction Probability weight a b s t r a c t Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations o...

The paper deals with the problem of the fuzzy data clustering. In other words, objects attributes can be represented by fuzzy numbers or fuzzy intervals. A direct algorithm of possibilistic clustering is the basis of an approach to the fuzzy data clustering. The paper provides the basic ideas of the method of clustering and a plan of the direct possibilistic clustering algorithm. Definitions of...

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...