Clustering algorithms have been widely used artificial intelligence, data mining and machine learning, etc. It is unsupervised classification and is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, to analysis interval data needs Type II fuzzy log...

Melon plants are that susceptible to disease, both diseases caused by viruses and those bacteria. One part of the plant can be affected disease is leaves. Leaves on diseased generally change color which will then affect other leaves inhibit development growth these plants. This study aims classify melon from leaf images. The data used in this 160 images grouped into several groups healthy group...

This research applied fuzzy c-means and fuzzy integral theories to a proposed novel two-step machinery fault diagnosis model. Distributed multiple fuzzy c-means classifiers were used to produce an initial diagnosis result by considering different features. Fuzzy measure and fuzzy integral data fusion theory was then applied to combine the initial diagnosis results into a consensus final decisio...

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...