Hyperellipsoidal Clustering Method for Building Fuzzy Models

A fuzzy array-based clustering method in team building

An array-based clustering approach is proposed as an effective mean for providing an alternative solution in team building problem. The proposed approach generates a systematic formation of task and team member families by sequencing the rows and the columns of a task-personnel incidence matrix. To obtain the task-personnel incidence matrix, a fuzzy sets approach will be proposed to combine two...

Spatial Models for Fuzzy Clustering

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the stren...

A new method for solving fuzzy DEA models

An efficient hyperellipsoidal clustering algorithm for resource-constrained environments

Clustering has been widely used as a fundamental data mining tool for the automated analysis of complex datasets. There has been a growing need for the use of clustering algorithms in embedded systems with restricted computational capabilities, such as wireless sensor nodes, in order to support automated knowledge extraction from such systems. Although there has been considerable research on cl...

Generalized Fuzzy Clustering Method

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c-means (FCM) method as well as the fuzzy c-median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The...