To identify T-S models, this paper presents a so-called “subtractive fuzzy C-means clustering” approach, in which the results of subtractive clustering are applied to initialize clustering centers and the number of rules in order to perform adaptive clustering. This method not only regulates the division of fuzzy inference system input and output space and determines the relative member functio...

Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like th...

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze th...

This article presents a study of academic performance evaluation using soft computing techniques inspired by the successful application of K-means, fuzzy C-means (FCM), subtractive clustering (SC), hybrid subtractive clustering-fuzzy C-means (SC-FCM) and hybrid subtractive clustering-adaptive neuro fuzzy inference system (SC-ANFIS) methods for solving academic performance evaluation problems. M...

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

slope stability analysis is an enduring research topic in the engineering and academic sectors. accurate prediction of the factor of safety (fos) of slopes, their stability, and their performance is not an easy task. in this work, the adaptive neuro-fuzzy inference system (anfis) was utilized to build an estimation model for the prediction of fos. three anfis models were implemented including g...

The clustering algorithm hybridization scheme has become of research interest in data partitioning applications in recent years. The present paper proposes a Hybrid Fuzzy clustering algorithm (combination of Fuzzy C-means with extension and Subtractive clustering algorithm) for data classifications applications. The fuzzy c-means (FCM) and subtractive clustering (SC) algorithm has been widely d...

shear wave velocity (vs) data are key information for petrophysical, geophysical and geomechanical studies. although compressional wave velocity (vp) measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. furthermore, measurement of shear wave velocity is to some extent costly. this study proposes a novel methodolo...

Cluster analysis has the aim of grouping several objects observation based on data found in information to describe and their relationships. The method used this research is Fuzzy C-Means (FCM) Subtractive (SFCM) methods. two methods were applied people's welfare indicator 42 regencies/cities island Kalimantan. purpose study was obtain results districts/cities Kalimantan indicators a comparison...