In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

According to the standard fuzzy C-means clustering algorithm performed poor in the clustering effect during the clustering process. This paper presents an objective function optimization based on the attribute weighted and the objective function optimization. Firstly, use a little prior knowledge as the labeled sample. These calibrated samples information are used as the prior knowledge, and th...

Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this propo...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of ove...

This paper describes a hybrid approach of Fuzzy C-means clustering and Genetic Algorithm (GA) is proposed that provides better accuracy & increases the intrusion detection rate. This approach provides better accuracy of detection as compared to K-means and FCM Clustering. With this proposed approach intrusion detection rate is improved considerably.A brief overview of a hybrid approach of genet...

The Human Development Index (HDI) is very important in measuring the country's success as an effort to build quality of life people a region, including Indonesia. government needs make groupings based on city/district. To facilitate data grouping similarity existing characteristics, it necessary have method, namely clustering technique. There are several algorithms that often used techniques, K...

The well-known fuzzy c-means algorithm is an objective function based fuzzy clustering technique that extends the classical k-means method to fuzzy partitions. By replacing the Euclidean distance in the objective function other cluster shapes than the simple (hyper-)spheres of the fuzzy c-means algorithm can be detected, for instance ellipsoids, lines or shells of circles and ellipses. We propo...