Fuzzy c-means (FCM) clustering is based on minimizing the fuzzy within cluster scatter matrix trace but FCM neglects the between cluster scatter matrix trace that controls the distances between the class centroids. Based on the principle of cluster centers separation, fuzzy cluster centers separation (FCCS) clustering is an extended fuzzy c-means (FCM) clustering algorithm. FCCS attaches import...

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of dista...

Clustering algorithms have proven to be a useful tool extract knowledge and support decision making by processing large volumes of data. Hard fuzzy clustering been used successfully identify patterns trends in many areas, such as finance, healthcare, marketing. However, these significantly increase their solution time the size datasets solved increase, use unfeasible. In this sense, parallel ha...

Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the perf...

Many data sets to be clustered are stored in relational databases. Having a clusterization algorithm implemented in SQL provides easier clusterization inside a relational DBMS than outside with some alternative tools. In this paper we propose Fuzzy c-Means clustering algorithm adapted for PostgreSQL open-source relational DBMS.

Fuzzy c-means clustering and its derivatives are very successful on many clustering problems. However, fuzzy c-means clustering and similar algorithms have problems with high dimensional data sets and a large number of prototypes. In particular, we discuss hard c-means, noise clustering, fuzzy c-means with a polynomial fuzzifier function and its noise variant. A special test data set that is op...

In general, road lane detection from a traffic surveillance camera is done by the analysis of geometric shapes of the road. Thus, Hough transform or B-snake technology is preferred to intelligent pattern matching or machine learning such as neural network. However, we insist that the feasibility of using intelligent technique in this area is quite undervalued. In this paper, we first divide the...

In speaker verification, a claimed speaker’s score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker’s and the impostors’ likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better s...

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...