Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock prediction. Especially, parameters in FCM have influence on results. However, a lot of did not solve the problem, that is, how to set parameters. In this study, we present kind method for computing values according role process. Ne...

This work proposes how to generate a set of fuzzy rules from a data set using a clustering algorithm, the GKPFCM. If we recommend a number of clusters, the GKPFCM identifies the location and the approximate shape of each cluster. These ones describe the relations among the variables of the data set, and they can be expressed as conditional rules such as "If/Then". The GKPFCM provides membership...

— Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. O...

This paper proposes a new clustering algorithm in the fuzzy-c-means family, which is designed to cluster time series and is particularly suited for short time series and those with unevenly spaced sampling points. Short time series, which do not allow a conventional statistical model, and unevenly sampled time series appear in many practical situations. The algorithm developed here is motivated...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Two-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k -means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode cl...

We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local...