Book ChapterDOI
Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014
Janmenjoy Nayak,Bighnaraj Naik,Himansu Sekhar Behera +2 more
- pp 133-149
TLDR
A comprehensive survey on FCM and its applications in more than one decade has been carried out to show the efficiency and applicability in a mixture of domains and to encourage new researchers to make use of this simple algorithm.Abstract:
The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various areas have been effectively solved by using FCM and its different variants. But, for efficient use of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains. Also, another intention of this survey is to encourage new researchers to make use of this simple algorithm (which is popularly called soft classification model) in problem solving.read more
Citations
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Journal ArticleDOI
A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation
Le Hoang Son,Tran Manh Tuan +1 more
TL;DR: Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant methods, which shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics.
Journal ArticleDOI
Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices
TL;DR: A new fuzzy clustering algorithm based on the neutrosophic orthogonal matrices for segmentation of dental X-Ray images and the experimental validation on real dental datasets of Hanoi Medical University Hospital, Vietnam showed the superiority of the proposed method against the relevant ones in terms of clustering quality.
Journal ArticleDOI
WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering
TL;DR: A method of data clustering using the WGC algorithm that determines the optimal centroid for performing the clustering process, which proves that the proposed WGC outperforms the existing methods.
Journal ArticleDOI
Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach
Mehrbakhsh Nilashi,Parveen Fatemeh Rupani,Mohammad Mobin Rupani,Hesam Kamyab,Weilan Shao,Hossein Ahmadi,Tarik A. Rashid,Nahla Aljojo +7 more
TL;DR: This research is the first attempt to employ fuzzy clustering and supervised machine learning techniques to country sustainability assessment to reveal the relationships between human sustainability, ecological sustainability and overall sustainability performance by discovering the decision rules.
Journal ArticleDOI
Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints
TL;DR: A novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation that has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods is proposed.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Book
Introduction to Data Mining
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, and some of the techniques used to solve these problems.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.