In this research, the traditional fuzzy ISODATA ( FI ) algorithm is integrated with probabilistic relaxation labeling ( PRL ) algorithm to form a new clustering algorithm called relaxed fuzzy ISODATA ( RFI ). During the clustering process, both the fuzzy membership function values and local contextual information are employed for grouping data into clusters. The RFI algorithm is considered for ...

Fuzzy cellular automata (FCA) are continuous cellular automata where the local rule is defined as the “fuzzification” of the local rule of a corresponding Boolean cellular automaton in disjunctive normal form. In this paper, we consider circular FCA; their asymptotic behaviours had previously been observed through simulation and FCA had been empirically classified accordingly. However, no analy...

Combined with weight of samples and kernel function, fuzzy clustering method with generalized entropy is studied. Objective function for fuzzy clustering with generalized entropy based on sample weighting is obtained. Following that, fuzzy clustering algorithm with generalized entropy based on sample weighting is presented. In addition, by introducing kernel into the presented objective functio...

It is a pivotal task how to organize and manage resources for Grid researchs and applications.This paper first proposes Grid resource management strategies by employing τ -parameter fuzzy concept lattice and fuzzy concept trie and combines FCA theory with Grid resource management technology.The formal concept definitions of Grid and resource are given and τ -parameter fuzzy concept lattice mode...

assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. sspco optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. one of the things that smart algorithms are applied to solve is the problem ...

Fuzzy clustering has been used widely in education, statistics, engineering, communication...etc. The fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Extending Euclidean distance to Mahalanobis distance, GustafsonKessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were devel...

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

In this paper, we propose a new method to specify the sequence of parameter values for a fuzzy clustering algorithm by using Q-learning. In the clustering algorithm, we employ similarities between two data points and distances from data to cluster centers as the fuzzy clustering criteria. The fuzzy clustering is achieved by optimizing an objective function which is solved by the Picard iteratio...

In this work, performance of the Artificial Bee Colony Algorithm which is a recently proposed algorithm, has been tested on fuzzy clustering. We applied the Artificial Bee Colony (ABC) Algorithm fuzzy clustering to classify different data sets; Cancer, Diabetes and Heart from UCI database, a collection of classification benchmark problems. The results indicate that the performance of Artificial...

-Clustering algorithms are an integral part of both computational intelligence and pattern recognition. It is unsupervised methods for classifying data into subgroups with similarity based inter cluster and intra cluster. In fuzzy clustering algorithms, mainly used algorithm is Fuzzy c-means (FCM) algorithm. This FCM algorithm is efficient only for spherical data when the input of the data stru...