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R. Dwivedi, A. Kumar, S. K. Ghosh, P. S. Roy
2012 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The SCM and fuzzy kappa coefficients are used to major relative accuracies, while entropy is an absolute uncertainty indicator.  ...  =1, which gives highest accuracy from sub-pixel confusion uncertainty matrix (SCM) i.e. 96.27% and AWiFS entropy has been 0.71 using noise clustering without entropy based classifier.  ...  CLASSIFIERS AND ACCURACY ASSESSMENT APPROACHES 2.1 Fuzzy c-Means Approach (FCM) Fuzzy c-Means (FCM) was originally introduced by Bezdek (1981) .  ... 
doi:10.5194/isprsarchives-xxxix-b3-385-2012 fatcat:jcam5lmtvfc5xk5izj3azel24e

Fuzzy Clustering Methods with Rényi Relative Entropy and Cluster Size

Javier Bonilla, Daniel Vélez, Javier Montero, J. Tinguaro Rodríguez
2021 Mathematics  
Specifically, such Rényi divergence-based term is added to the variance-based Fuzzy C-means objective function when allowing cluster sizes.  ...  However, previous works making use of either Rényi entropy or divergence in fuzzy clustering, respectively, have not considered cluster sizes (thus applying regularization in terms of entropy, not divergence  ...  Fuzzy C-Means with Rényi Relative Entropy and Cluster Size Inputs: Dataset X = (X i ) i=1,...  ... 
doi:10.3390/math9121423 fatcat:a2gym5d6y5a7zc2rbbnbf4r5eq

A Rough-fuzzy C-means using information entropy for discretized violent crimes data

Chao Yang, Shiyuan Che, Xueting Cao, Yeqing Sun, Ajith Abraham
2013 13th International Conference on Hybrid Intelligent Systems (HIS 2013)  
This paper presents the factor clustering analysis for violent crimes. The efficiency of Rough-fuzzy C-means algorithm is affected by the numbers of clusters, and not all centroids are beneficial.  ...  In this paper, a novel discrete Rough-fuzzy C-means based on information entropy algorithm (DRFCMI) is proposed, which can obtain typical conclusions objectively.  ...  C. Fuzzy C-Means Fuzzy C-means (FCM) [10] derives from Hard C-means (HCM) [11] .  ... 
doi:10.1109/his.2013.6920495 dblp:conf/his/YangCCSA13 fatcat:lrmtdxc7onaunnis2qihhyht2e

Hybrid Medical Image Segmentation based on Fuzzy Global Minimization by Active Contour Model

J. Umamaheswari, G. Radhamani
2012 International Journal of Computer Applications  
This paper provides new hybrid medical image segmentation based on Global Minimization by Active Contour (GMAC) method and Spatial Fuzzy C Means Clustering method (SFCM) tailored to CT imaging applications  ...  Here globalization of contour is applied which normalizes the threshold to form a cluster by spatial fuzzy means.  ...  In this paper GMAC combined with SFCM is proposed. hybrid method that combines region-based fuzzy clustering method called Enhanced Possibility Fuzzy C Means (EPFCM) and Generalized Gradient vector flow  ... 
doi:10.5120/4877-7309 fatcat:5xvb7u2unfewxi6wdqholvoyhy

Fuzzy Pattern Recognition Based on Symmetric Fuzzy Relative Entropy

Y.F. Shi, L.H. He, J. Chen
2009 International Journal of Intelligent Systems and Applications  
Based on fuzzy similarity degree, entropy, relative entropy and fuzzy entropy, the symmetric fuzzy relative entropy is presented, which not only has a full physical meaning, but also has succinct practicability  ...  Index Terms-pattern recognition, fuzzy set, fuzzy similarity degree, relative entropy, symmetric fuzzy relative entropy, divergence  ...  The symmetric fuzzy relative entropy presented in this paper has not only clear physical meaning but also practical usage.  ... 
doi:10.5815/ijisa.2009.01.08 fatcat:pq47ufi3wjfvjhs2fbmpidtlum

An Improved K-means Algorithm Based on Fuzzy Metrics

Xinyu Geng, Yukun Mu, Senlin Mao, Jinchi Ye, Liping Zhu
2020 IEEE Access  
.: An Improved K-means Algorithm Based on Fuzzy Metrics SENLIN MAO received the bachelorâȂŹs degree in City Underground Space Engineering from Southwest Petroleum University, Chengdu, Sichuan, China, where  ...  fuzzy clustering algorithm on entropy (FCMOE) 250 with fuzzy entropy constraints.  ...  c-mean. 119 In reference [35] , a novel fuzzy-entropy based clustering 120 measure (FECM) is presented, in which the average sym-121 metric fuzzy cross entropy of membership subset pairs is 122 integrated  ... 
doi:10.1109/access.2020.3040745 fatcat:7i7wu5or6bfwpl7rlvusvjff2y

Investigation of Ambient Factors Spatial Variability in Cold Stores by Using Management Zone Analysis

Serap Akdemir
2014 Journal of Agricultural Sciences  
The measured data were analysed by MZA software which performed fuzzy clustering to delineate the full cold storage, half full cold storage and empty cold storage.  ...  Ambient temperature, relative humidity and air velocity of an experimental cold store were measured using 36 temperature-relative humidity sensors and air velocity measurement probe.  ...  The k-means (also known as c-means) is the most important non-hierarchical clustering.  ... 
doi:10.15832/tbd.67012 fatcat:cin74rhbjzh4jll3dvygm72gk4

Comparision of performance of multi criteria decision making ensemble-clustering algorithms in rainfall frequency analysis

Nilotpal Debbarma, Parthasarathi Choudhury, Parthajit Roy
2021 Water practice and technology  
and Fuzzy C-Means for comparision.  ...  K-Means and Fuzzy C-Means are commonly used methods in regionalization of rainfall, but application of genetic algorithm is very rarely explored.  ...  Shannon entropy weights were found accurate for Figure 5 | Regional relative rmse of regional growth curve in regions obtained by GA based clustering, K-Means, Fuzzy C-Means and IMD Pune.  ... 
doi:10.2166/wpt.2021.086 fatcat:eagpwybc6ndr7o6iftwh6vsoie

Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data

Iwan Tri Riyadi Yanto, Ririn Setiyowati, Edi Sutoyo, Nur Azizah, Rasyidah
2021 JOIV: International Journal on Informatics Visualization  
This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence.  ...  Fuzzy k-means algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function.  ...  Section II describes Fuzzy k-Means, Entropy, and KL Divergence. Section III explains the proposed method based on KL Divergence to fuzzy k-Means Algorithm.  ... 
doi:10.30630/joiv.5.1.462 fatcat:qzigdenyzfcfbkczq62anavq44

Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

Hoda Khanali, Babak Vaziri
2021 Statistics, Optimization and Information Computing  
Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM).  ...  This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that  ...  Fuzzy clustering has important applications in image processing, pattern recognition, object recognition, and so on [1, 2, 3] other similar techniques as fuzzy c-means [4] , possibilistic fuzzy c-means  ... 
doi:10.19139/soic-2310-5070-1035 fatcat:66fpl3alxbfadilqqw5gtpgoli

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

Yuan Huang, Zhe Cheng, Qianyu Zhou, Yuxing Xiang, Ruixiao Zhao
2020 IEEE Access  
We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy.  ...  INDEX TERMS Artificial intelligence, data mining, cluster analysis, scalable parallel fuzzy c-means, cloud computing.  ...  Second, the traditional fuzzy c-means clustering algorithm, fuzzy c-means clustering algorithm with fuzzy entropy, and scalable parallel fuzzy c-means clustering algorithm are applied to simple data sets  ... 
doi:10.1109/access.2020.2981632 fatcat:auwfsg4mvzgjrd6gyutt2ye3q4

Mutual Information Analysis with Similarity Measure

Hong-Mei Wang, Sang-Hyuk Lee
2010 International Journal of Fuzzy Logic and Intelligent Systems  
Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure.  ...  Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors.  ...  By the meaning of fuzzy entropy, the entropy value approaches zero, the student group has a higher tendency toward B and C grade.  ... 
doi:10.5391/ijfis.2010.10.3.218 fatcat:uyovbo64knh5ndz674d6voto2i

Image Segmentation using Neural Network and Modified Entropy

Bingquan Huo, Fengling Yin
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
In this paper, a novel image segmentation algorithm based on fuzzy clustering and entropy analysis using space information for optical images is proposed.  ...  The result indicates that compared to FCM and some other clustering methods, our entropy and neural network based algorithm performs better.  ...  We can find out the phenomenon from the previous two formulas that the cluster center and membership degree are relative to each other, and therefore, iterative approach as adopted by the fuzzy c-means  ... 
doi:10.14257/ijsip.2015.8.3.23 fatcat:ivtx7s3lwrflvb3k7io57hjmsi

Investigation of spatial variability of air temperature, humidity and velocity in cold stores by using management zone analysis

2014 Journal of Agricultural Sciences  
The measured data were analysed by MZA software which performed fuzzy clustering to delineate the full cold storage, half full cold storage and empty cold storage.  ...  Ambient temperature, relative humidity and air velocity of an experimental cold store were measured using 36 temperature-relative humidity sensors and air velocity measurement probe.  ...  The k-means (also known as c-means) is the most important non-hierarchical clustering.  ... 
doi:10.1501/tarimbil_0000001277 fatcat:ewhlytyftja5bkb52aog6mg67e

Assessing the Quality of Fuzzy Partitions Using Relative Intersection

2005 IEICE transactions on information and systems  
validity, fuzzy clustering, fuzzy c-means Dae-Won Kim received the M.S. and  ...  Based on these considerations, a new cluster validity index is proposed for fuzzy partitions obtained from the fuzzy c-means algorithm.  ...  Of the fuzzy clustering methods developed to date, the fuzzy c-means (FCM) algorithm [1] is the most widely used.  ... 
doi:10.1093/ietisy/e88-d.3.594 fatcat:infvprw6mjacde6ceagkfe5wrq
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