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Bulanık Kümeleme Analizinde Parametre Seçiminin Etkisi

Ozer Ozdemir, Asli Kaya
2018 Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi  
In this work, in order to overcome this problem, cluster validity indices in literature were reviewed and these indices were used in genetic data set.  ...  Clustering can be performed in hard or fuzzy mode. One of the important conditions in order to reach accurate results in clustering analysis is to determine the initial parameters.  ...  Fukuyama and Sugeno Index (FS) Validity function proposed by Fukuyama and Sugeno is defined by (Fukuyama and Sugeno, 1989 )     2 2 1 1 1 1 1 where ,, FS m m n c n c mm ij j i ij i j i i c i j i V  ... 
doi:10.31200/makuubd.348688 fatcat:bflnketjg5gi5dp3wyjdu2d7xe

A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

Javier Senent-Aparicio, Jesús Soto, Julio Pérez-Sánchez, Jorge Garrido
2017 Journal of Hydrology and Hydromechanics  
Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used  ...  Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters.  ...  For Fuzzy C-Means Approach, optimum numbers of clusters were analysed by using four fuzzy cluster validity indices, namely partition coefficient, partition entropy, Xie-Beni index and Fukuyama-Sugeno on  ... 
doi:10.1515/johh-2017-0024 fatcat:7en7sstr5zc5jiqsmk3h4bj6wm

Minimal Spanning Tree Based Fuzzy Clustering

Ágnes Vathy-Fogarassy, Balázs Feil, János Abonyi
2007 Zenodo  
The calculated similarities of the clusters can be used for the hierarchical clustering of the resulted fuzzy clusters, which information is useful for cluster merging and for the visualization of the  ...  For the analysis of the resulted fuzzy clusters a new fuzzy similarity measure based tool has been presented.  ...  There is an extension of Fukuyama-Sugeno index, which involves the membership values and also the dataset.  ... 
doi:10.5281/zenodo.1055913 fatcat:wjpur3z4rnfnteqmdz475r47be

Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

Miloš Gligorić, Zoran Gligorić, Čedomir Beljić, Slavko Torbica, Svetlana Štrbac Savić, Jasmina Nedeljković Ostojić
2016 Energies  
The relative closeness is obtained by the TOPSIS method while technological clusters are formed by fuzzy C-mean clustering.  ...  The main aim of a coal deposit model is to provide an effective basis for mine production planning.  ...  Selection of the optimal way of clustering is based on the comparison of the obtained adjusted Rand indexes, entropies and Fukuyama-Sugeno validity functionals.  ... 
doi:10.3390/en9121059 fatcat:dqbphe7oenebzmoo7eotsp7jte

A Novel Data Clustering Algorithm based on Modified Adaptive Particle Swarm Optimization

Ganglong Duan, Wenxiu Hu, Zhiguang Zhang
2016 International Journal of Signal Processing, Image Processing and Pattern Recognition  
Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a point in large data sets belongs to two or more clusters.  ...  On the one hand, Particle Swarm Optimization is proven to be an effective and robust technique for fuzzy clustering.  ...  Acknowledgements This research is supported by the youth fund of national natural science fund project: information technology based on the dynamic adjustment speed economic value measurement and validation  ... 
doi:10.14257/ijsip.2016.9.3.16 fatcat:5tvewfxojbhkzpm6li4c72cuea

Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters

Min Chen, Simone A. Ludwig
2014 Journal of Artificial Intelligence and Soft Computing Research  
Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters.  ...  Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined.  ...  Fukuyama and Sugeno (FS) Index Fukuyama and Sugeno proposed a validity function in 1989 [38] . It is defined as (14) wherec = ∑ c j=1 c j /c. It measures the separation.  ... 
doi:10.2478/jaiscr-2014-0024 fatcat:djqpzxmbi5c3jnwwgqdxfz56be

Fuzzy clustering using automatic particle swarm optimization

Min Chen, Simone A. Ludwig
2014 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a data point to belong to two or more clusters.  ...  Fuzzy c-means is one of the most well-known and used methods, however, the number of clusters need to be defined in advance.  ...  in 1995 [31] . 5) Fukuyama and Sugeno (FS) Index: Fukuyama and Sugeno proposed a validity function in 1989 F S = n i=1 c j=1 µ m ij ||x i − c j || − n i=1 c j=1 µ m ij ||c j −c|| XB = J m n × min i  ... 
doi:10.1109/fuzz-ieee.2014.6891874 dblp:conf/fuzzIEEE/ChenL14 fatcat:smmhwdy2wzc3higbtzjycdjnpy

Comparison of FCM and FISODATA

B. Fergani, Mohamed-Khireddine Kholladi, M. Bahri
2012 International Journal of Computer Applications  
In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method.  ...  An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step.  ...  Many clustering methods have been used (see [1] ), such as the hard clustering methods and the fuzzy clustering methods.  ... 
doi:10.5120/8913-2960 fatcat:sxsuepi45jfqdcqf3zgbzjo3va

Effective Image Segmentation using a Locally Weighted Fuzzy C-Means Clustering
지역 가중치 적용 퍼지 클러스터링을 이용한 효과적인 이미지 분할

Nyma Alamgir, Jong-Myon Kim
2012 Journal of the Korea Society of Computer and Information  
This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image  ...  The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the  ...  ) and Fukuyama-Sugeno function (Vfs).  ... 
doi:10.9708/jksci/2012.17.12.083 fatcat:5wvg4himgbdgllnyfxqidklmjm

Improving fuzzy c-means clustering via quantum-enhanced weighted superposition attraction algorithm

Adil Baykasoğlu, İlker Gölcük, Fehmi Burçin Özsoydan
2019 Hacettepe Journal of Mathematics and Statistics  
Fuzzy clustering has become an important research field in pattern recognition and data analysis.  ...  The results point out significant improvements over the traditional fuzzy c-means algorithm.  ...  In this study, we have selected partition coefficient, partition entropy, validity index of Chen and Linkens, validity index of Fukuyama and Sugeno, and the validity index of Xie and Beni.  ... 
doi:10.15672/hjms.2019.655 fatcat:ddtommxm6zcqhnhk5eoyr7euve

Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing

Kai Xiao, Sooi ck Ho, Andrzej Bargiela
2010 International Journal of Computational Intelligence in Bioinformatics and Systems Biology  
The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise.  ...  In addition to the observations on the clustering results of the MR images, we use validity functions and clustering centroids to evaluate the clustering results.  ...  To quantify the ratio of total variation within clusters and the separation of clusters, Fukuyama and Sugeno (1989) proposed Fukuyama-Sugeno validity function V fs, , Xie and Beni (Xie and Beni, 1991  ... 
doi:10.1504/ijcibsb.2010.031393 fatcat:xy3eixb6evbrhmexs5n4zurzva

A Validity Index for Fuzzy Clustering Based on Bipartite Modularity

Yongli Liu, Xiaoyang Zhang, Jingli Chen, Hao Chao
2019 Journal of Electrical and Computer Engineering  
Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we propose a validity index for fuzzy clustering, named CSBM (compactness separateness  ...  Experimental results show that the CSBM index performs the best in terms of robustness while accurately detecting the number of clusters.  ...  At the same time, the validity index used to measure the clustering quality of fuzzy clustering, as an indispensable part of algorithm research, plays a growing important role in fuzzy clustering.  ... 
doi:10.1155/2019/2719617 fatcat:bldezlrbvzdqtj4buyvo5qydoe

Fcm Parameter Estimation Methods: Application To Infrared Spectral Histology Of Human Skin Cancers

Cyril Gobinet, Teddy Happillon, Pierre Jeannesson, Michel Manfait, Olivier Piot, David Sebiskveradze, Valeriu Vrabie
2012 Zenodo  
The most efficient indices in the scientific literature are the following ones [6] : the Fukuyama-Sugeno index (V FS ), the Xie-Beni index (V XB ), the Kwon index (V Kw ), the Tang index (V T ), the partition  ...  In this article, FCM-RBA is modified in order to improve the computational time.  ... 
doi:10.5281/zenodo.52107 fatcat:v7scl7ek6nhtjbkd4put5qoq7u

Stability-integrated Fuzzy C means segmentation for spatial incorporated automation of number of clusters

V ROYNA DAISY, S NIRMALA
2018 Sadhana (Bangalore)  
Experiments are performed on synthetic and real images and the number of clusters determined is validated using validation indices.  ...  Determining the number of clusters and including spatial information to basic Fuzzy C Means clustering are done in numerous ways.  ...  Fukuyama-Sugeno index The Fukuyama-Sugeno index (FS) [26] is defined as FS ¼ X N j¼1 X C i¼1 u m ij x j À c i 2 À c i À c k k 2 ð8Þ where c ¼ X C i¼1 c i C : ð9Þ The FS calculation is based on the compactness  ... 
doi:10.1007/s12046-018-0802-5 fatcat:uj3m6myxorbmbk3hmoenhyhvy4

A cluster validity index for fuzzy clustering

Kuo-Lung Wu, Miin-Shen Yang
2005 Pattern Recognition Letters  
It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors to create the PCAES validity index.  ...  Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms.  ...  Indexes in this class include the XB index proposed by Xie and Beni (1991) , FS index proposed by Fukuyama and Sugeno (1989) , SC index proposed by Zahid et al. (1999) , the fuzzy hypervolume (FHV)  ... 
doi:10.1016/j.patrec.2004.11.022 fatcat:lpi6ajrfbjcofpsxfho7utbd3u
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