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Automated Mapping of Hydrographic Systems from Satellite Imagery Using Self-Organizing Maps and Principal Curves [chapter]

Marek B.
2011 Self Organizing Maps - Applications and Novel Algorithm Design  
Fuzzy C-means was the first algorithm to incorporate fuzzy login in the learning process.  ...  The combination of SOM and fuzzy C-means can overcome the drawbacks of both methods and improve the performance of classification.  ... 
doi:10.5772/13927 fatcat:epmgvixwrndurcjorx2jx5jxgm

Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach [article]

Suchismita Das, Nikhil R. Pal
2020 arXiv   pre-print
In this context, we also propose a new variant of the Geodesic c-means clustering algorithm.  ...  Then, we extend this concept to provide a general framework for learning an unsupervised fuzzy model for data projection with different objective functions.  ...  A natural choice for the clustering algorithm appears to be the c-means (often called k-means) or the fuzzy c-means (FCM).  ... 
arXiv:2004.03922v1 fatcat:euhjztiifbhvpehb75ee2shify

An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imagery

Hongzan Jiao, Yanfei Zhong, Liangpei Zhang
2014 IEEE Transactions on Geoscience and Remote Sensing  
The traditional clustering methods of k-means, ISODATA, fuzzy c-means classifier, and FCM and MoDEFC after principal component analysis transformation are provided to compare with the UADSM classifier,  ...  Finally, a reasonable category for each spectral signature is automatically identified by the normalized spectral DNA similarity norm.  ...  such as k-means, iterative self-organizing data (ISODATA), and fuzzy c-means clustering algorithms, which originate from multispectral imagery.  ... 
doi:10.1109/tgrs.2013.2282356 fatcat:7owvypz5qrfwvdk4aphkkvprkq

Quantitative Analysis of Internet of Things Technology on the National Economic Accounting: A Prediction Model Based on the FCM-BP Neural Network Algorithm

Jian-Tao Song, Gopal Chaudhary
2022 Computational Intelligence and Neuroscience  
For national economic accounting, a new integrated system for monitoring the environment is developed and designed by using embedded development technology and sensor technology.  ...  The system uses a wireless sensor network environment monitoring system IoT platform with embedded internal processors.  ...  C-means clustering algorithm to the evaluation of national economic benefits. erefore, this paper will construct a national economic benefit evaluation system based on fuzzy C-means cluster analysis algorithm  ... 
doi:10.1155/2022/5335310 pmid:35571718 pmcid:PMC9098280 fatcat:dkylrovmrbezjo3ic7g7ai3hsm

Transformer failure diagnosis by different rule extraction method: a review

Anurag Tamrakar, V.B. Reddy
2018 International Journal of Advanced Technology and Engineering Exploration  
[18] presented a transformer failure diagnosis system based on DGA. It is done by the extraction of the rules from Kohonen Self-Organizing Map. In this process the Kohonen net was trained first.  ...  They have presented fuzzy C-Means (FCM) algorithm for the detection of OLTC faults in timely manner. Embedding dimension and delay time have been obtained by applying Cao's method.  ... 
doi:10.19101/ijatee.2018.538002 fatcat:6sdwolu6jjhwlasvtl3tl4m4iy

Big data clustering techniques based on Spark: a literature review

Mozamel M. Saeed, Zaher Al Aghbari, Mohammed Alsharidah
2020 PeerJ Computer Science  
Moreover, we propose a new taxonomy for the Spark-based clustering methods. To the best of our knowledge, no survey has been conducted on Spark-based clustering of Big Data.  ...  This survey also highlights the new research directions in the field of clustering massive data.  ...  A distributed possibilistic c-means algorithm is proposed in Zhang et al. (2019) .  ... 
doi:10.7717/peerj-cs.321 pmid:33816971 pmcid:PMC7924475 fatcat:hmpihu6qvncsbammunbkmlybma

A survey of fuzzy logic in wireless localization

Marwan Alakhras, Mourad Oussalah, Mousa Hussein
2020 EURASIP Journal on Wireless Communications and Networking  
Fuzzy c-means (FCM) algorithm, which is proposed by Bezdek [116, 117] , is one of the most extensively applied fuzzy clustering algorithms.  ...  C-Mean Carry out fuzzy partition Not reported Not reported 5 inputs Not reported Not reported [34] Fuzzy Position estimation and weighting kNN TKS Trapezoidal 2 inputs 32 rules Not  ... 
doi:10.1186/s13638-020-01703-7 fatcat:wqccddma6ng33bb6jcqyjjt5ym

State of the art document clustering algorithms based on semantic similarity

Karwan Jacksi, Niyaz Salih
2020 Jurnal Informatika  
Avanija and Ramar proposed a C-Means Fuzzy technique called Semantic Hybrid Ontology Document Clustering (SEMHYBODC).  ...  Different knowledge is then grouped using Fuzzy C -Means Clustering Algorithm [35] .  ... 
doi:10.26555/jifo.v14i2.a17513 fatcat:g7wvf4xxzvczthl5qut2al72dm

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Lefeng Cheng, Tao Yu
2018 Energies  
into a local optimum.  ...  approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy  ...  For the application of fuzzy C-means algorithm, Fu et al.  ... 
doi:10.3390/en11040913 fatcat:5abufqmt7rcrlkquxncgxame2m

Industrial applications of soft computing: a review

Y. Dote, S.J. Ovaska
2001 Proceedings of the IEEE  
A. Zadeh proposed the concept in the early 1990s.  ...  Fuzzy logic (FL), neural networks (NN), and evolutionary computation (EC) are the core methodologies of soft computing.  ...  The learning of the weights was carried out from the results of a fuzzy C-means clustering algorithm. Control of the annealing furnace was achieved by mixing a static inverse model of the Table 9 .  ... 
doi:10.1109/5.949483 fatcat:erzs7fqa35dixgm7mq2efptpai

Wavelet-based vector quantization for high-fidelity compression and fast transmission of medical images

Sunanda Mitra, Shuyu Yang, Vadim Kustov
1998 Journal of digital imaging  
This new adaptive vector quantization uses a neural networks-based clustering technique for efficient quantization of the wavelet-decomposed subimages, yielding minimal distortion in the reconstructed  ...  The performance of a recently designed wavelet-based adaptive vector quantization is compared with a well-known waveletbased scalar quantization technique to demonstrate the superiority of the former technique  ...  centroids are updated by ah optimization constraint, including fuzzy membership values of the data samples following the fuzzy C means (FCM) 16J7 nonlinear equations.  ... 
doi:10.1007/bf03168174 pmid:9848058 pmcid:PMC3453325 fatcat:indq3p3sdngfbertlyre4eso7e

A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain [article]

Sudipta Roy, Sanjay Nag, Indra Kanta Maitra, Samir Kumar Bandyopadhyay
2013 arXiv   pre-print
Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.  ...  There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images.  ...  Figure 17 : 17 Brain tumor segmentation by fuzzy C-means results a) and c) original brain MR images, b) and d) output produce by fuzzy c-means: For the first image tumor detect but produce noise and for  ... 
arXiv:1312.6150v1 fatcat:fspy3az4uffwjbwvjo75rjwdlu

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Hao-Zhi Huang, Kun Xu, Ralph R. Martin, Fei-Yue Huang, Shi-Min Hu
2016 IEEE Transactions on Image Processing  
To address this problem, we initialize ICM with a palette generated by a modified mediancut method.  ...  As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one.  ...  Representative clustering approaches have been based on median-cut [2] , octrees [3] , self-organizing maps [4] , minmax [10] , k-means [1] , fuzzy c-means [11] , adaptive distributing units [12  ... 
doi:10.1109/tip.2015.2513599 pmid:26731765 fatcat:tmcur2i2q5atrm7pnndx5oxtxy

Optimization problems in electron microscopy of single particles

C. O. S. Sorzano, R. Marabini, A. Pascual-Montano, S. H. W. Scheres, J. M. Carazo
2006 Annals of Operations Research  
In particular, it focuses on a methodology known as "single-particles" and makes a thorough review of all those steps that can be expressed as an optimization problem.  ...  In spite of important advances in recent years, there are still unresolved challenges in the field that offer an excellent testbed for new and more powerful optimization techniques.  ...  The new algorithm is called Smoothly distributed Fuzzy C-means, or FuzzySOM for short.  ... 
doi:10.1007/s10479-006-0078-8 fatcat:vulyxxri45fw3idlzumicndrym

An interpretable evolving fuzzy neural network based on self-organized direction-aware data partitioning and fuzzy logic neurons

Paulo Vitor de Campos Souza, Edwin Lughofer, Augusto Junio Guimaraes
2021 Applied Soft Computing  
Guimaraes, An interpretable evolving fuzzy neural network based on self-organized direction-aware data partitioning and fuzzy logic neurons, Applied Soft Computing (2021), doi: https://doi.J o u r n a  ...  The main highlights of our new approach are: -Construction of a model capable of working online, updating its model hyperparameters and architecture based on dataset information.  ...  Versions of fuzzy c-means [63] , ANFIS [9] , and clustering by the cloud [64] are commonly implemented.  ... 
doi:10.1016/j.asoc.2021.107829 fatcat:lmsrwpb7qfe33bbh76hdij2gou
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