Kernel Entropy Based Fuzzy C-Means (KEFCM) for Acute Sinusitis

Semi-supervised Kernel-Based Fuzzy C-Means

This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...

Incremental Kernel Fuzzy c-Means

The size of everyday data sets is outpacing the capability of computational hardware to analyze these data sets. Social networking and mobile computing alone are producing data sets that are growing by terabytes every day. Because these data often cannot be loaded into a computer’s working memory, most literal algorithms (algorithms that require access to the full data set) cannot be used. One ...

Kernel-based fuzzy and possibilistic c-means clustering

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

Fuzzy C-Means Algorithm with Divergence-Based Kernel

Relative entropy fuzzy c-means clustering

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...