Application of Fuzzy Clustering Iterative Model in Classification of Geologic Environment Quality

2012 ◽  
Author(s):  
Xuefeng Yu ◽  
Zhongyuan Zhang
Keyword(s):  
Fuzzy Clustering ◽  
Environment Quality ◽  
Iterative Model ◽  
Geologic Environment

scholarly journals Fuzzy clustering analysis to study geomagnetic coastal effects

2005 ◽  
Vol 23 (4) ◽  
pp. 1157-1163 ◽  
Author(s):  
M. Sridharan ◽  
N. Gururajan ◽  
A. M. S. Ramasamy
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Computer Technology ◽  
Clustering Analysis ◽  
East Coast ◽  
Equatorial Electrojet ◽  
Analytical Technique ◽  
Fuzzy Clustering Analysis ◽  
The Subject

Abstract. The utility of fuzzy set theory in cluster analysis and pattern recognition has been evolving since the mid 1960s, in conjunction with the emergence and evolution of computer technology. The classification of objects into categories is the subject of cluster analysis. The aim of this paper is to employ Fuzzy-clustering technique to examine the interrelationship of geomagnetic coastal and other effects at Indian observatories. Data from the observatories used for the present studies are from Alibag on the West Coast, Visakhapatnam and Pondicherry on the East Coast, Hyderabad and Nagpur as central inland stations which are located far from either of the coasts; all the above stations are free from the influence of the daytime equatorial electrojet. It has been found that Alibag and Pondicherry Observatories form a separate cluster showing anomalous variations in the vertical (Z)-component. H- and D-components form different clusters. The results are compared with the graphical method. Analytical technique and the results of Fuzzy-clustering analysis are discussed here.

Research on User Clustering of Recommended System Based on Fuzzy Clustering

2014 ◽  
Vol 513-517 ◽  
pp. 1540-1544
Author(s):  
Li Hua Zhang ◽  
Wei Liu
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Analysis ◽  
Recommendation System ◽  
Information Overload ◽  
Fuzzy Clustering Analysis ◽  
User Clustering ◽  
Objective Classification ◽  
Distance Education System ◽  
High Quality Service

Today's society is a society of information explosion, the popularity of the Internet and development bring a lot of convenience to people, people can easily get a lot of information on the network, however, facing so many information, people prone to the problems of "information overload" and "resources disorientation. Therefore, the recommended system came into being, the recommendation system can provide people with the most in need and most concern to avoid the time of the search and comparison. This article intends to use the very mature recommendation system in the field of electronic commerce to distance education system and promotes personalized learning, shifting the traditional "what teachers teach, what students receive" to "what the students need, what the system provides, which is consistent of constructivism study philosophy. The analysis of users interested as the basis of the recommendation system, users clustering is very important, the objective classification of fuzzy clustering analysis can recommend for users to enjoy high-quality service.

COMPARISON OF TYPE-2 FUZZY CLUSTERING-BASED CASCADE CLASSIFIER MODELS FOR ECG ARRHYTHMIAS

2014 ◽  
Vol 26 (06) ◽  
pp. 1450075
Author(s):  
Rahime Ceylan ◽  
Yüksel Özbay ◽  
Bekir Karlik
Keyword(s):  
Feature Extraction ◽  
Fuzzy Clustering ◽  
Classification Algorithms ◽  
Ecg Signal ◽  
Classification Models ◽  
Cascade Classifier ◽  
Training Set ◽  
Feature Sets

The aim of this study is to present a comparison of the novel cascade classifier models based on fuzzy clustering and feature extraction techniques according to efficiency. These models are composed of three subsystems: The first subsystem is constituted by fuzzy clustering technique to choose the best patterns that ideally show its class attributes in dataset. The second subsystem consists of discrete wavelet transform (DWT) which realizes feature extraction procedure on selected patterns by using fuzzy c-means clustering. The last subsystem implements the classification of extracted features for each pattern using classification algorithm. In this paper, type-2 fuzzy c-means (T2FCM) clustering is used in the first subsystem of the proposed classification models and the new training set is obtained. In the second subsystem, the features of the obtained new training set are extracted with DWT; hence, three different feature sets along with different number of features are formed using Daubechies-2 wavelet function. In the last subsystem of the model, the feature sets are classified using classification algorithm. Here, two different classification algorithms, neural network (NN) and support vector machine (SVM), are used for comparison. Thus, two classification models are implemented and named as T2FCWNN (classifier is NN) and T2FCWSVM (classifier is SVM), respectively. This proposed classifier models have been applied to classify electrocardiogram (ECG) signals. One of the goals of this study is to present a fast and efficient classifier. For this reason, high accuracy rate is been aimed for classification of RR intervals in ECG signal. So, we have utilized T2FCM and WTs to improve the performance of the classification algorithms. Both training and testing set for classifier models have included 12 ECG signal classes. Well-known back propagation algorithm has been used for training of neural networks (NNs). The best testing results have been obtained with 99% recognition accuracy with T2FCWNN-2.

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