The Construction of Cascaded Features and Its Application in Fuzzy Clustering The success of fuzzy clustering heavily relies on the proper feature space constructed by the input data. For nonspherical and overlapped clusters, kernel fuzzy clustering is more effective owing to it finds more proper feature space compared to conventional fuzzy clustering. Unfortunately, poor scalability of kernel fuzzy clustering is induced by the requirement of large memory and running time. To solve the problem, random feature based method was presented to approximate the kernel function. Features in this approximate feature space are very useful information. Inspired by the architecture of functionallink neural network, to represent the diversity of features, cascaded features are constructed by a new feature mapping technique called cascaded feature mapping in this paper. By performing classical fuzzy cmeans (FCM) with the cascaded features, a new fuzzy clustering algorithm called FCMCF is developed. The experimental results of our proposed methods verify the superiority in comparison of other classical fuzzy clustering methods.

An Evolutionary NeuroFuzzy Cmeans Clustering Technique Abstract   One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy Cmeans (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a NeuroFuzzy CMeans Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current stateoftheart clustering methods to demonstrate the efficacy of the proposed algorithm.