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Clustering Analysis for fMRI Dataset based on ISODATA Algorithm
2005 International Conference on Neural Networks and Brain, 2006In the paper, the modified fuzzy c-means (MFc) is firstly used to treat the ill-balanced fMRI dataset to improve the efficiency, remove the redundance and reduce the population of analyzed voxels. Then the iteration self-organization data analysis techniques algorithm (ISODATA) method, as the development of data-driving methods, is utilized to find out
null Xi Zheng +4 more
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On the Convergence of the Fuzzy Clustering Algorithm “Fuzzy ISODATA”
ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 1986AbstractIn der vorliegenden Arbeit wird für den unscharfen Partitionierungsalgorithmus “Fuzzy ISODATA” gezeigt, daß ausgehend von beliebigem Startpunkt jeder Häufungspunkt der generierten Folge ein stationärer Punkt des erweiterten, Quadratischen‐Fehlersummen‐Funktionals ist, welches als Partitionierungskriterium dient.
von Trzebiatowski, G., Bank, B.
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Feature selection based on sensitivity analysis of fuzzy ISODATA
Neurocomputing, 2012A feature selection method based on sensitivity analysis and the fuzzy Interactive Self-Organizing Data Algorithm (ISODATA) is proposed for selecting features from high dimensional gene expression data sets. First, feature sensitivities for discriminating classes are calculated on the basis of the fuzzy ISODATA method.
Quanjin Liu +3 more
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An efficient parallel ISODATA algorithm based on Kepler GPUs
2014 International Joint Conference on Neural Networks (IJCNN), 2014ISODATA is a well-known clustering algorithm used in various areas. It employs a heuristic strategy allowing the clusters based on the nearest neighbor rule to split and merge as appropriate. However, since the volume of the data to be clustered in real world is growing continuously, the efficiency of serial ISODATA has become a serious practical issue.
Shiquan Yang, Jianqiang Dong, Bo Yuan
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An adaptive isodata fuzzy clustering algorithm with partial supervision
2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012Semi-supervised learning uses large amount of unlabeled data, combined with labeled data, to guide the learning process. This paper introduces a new clustering algorithm with partial supervision based on an adaptive distance. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive ...
Valmir Macario +1 more
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Curvelet Transform and ISODATA Thresholding for Retinal Vessel Extraction
2021Researchers says eyes are good indicator of many diseases like Diabetic retinopathy, Glaucoma, Hyper tension, cardiac disease and many age related abnormalities. Here we aim to put forward a reliable, fast and computerized method to get the vessel network of fundus image, that can assist the ophthalmologist to diagnose the disease in early stage and ...
Sakambhari Mahapatra +2 more
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Recovering valid clusters with ISODATA supervised by the CAIC
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1988The authors developed an unsupervised clustering method that is a variant of the well known ISODATA clustering algorithm. They replace the heuristic rules that control ISODATA with rules that search for the minimum value of an information theoretic criterion.
C.S. Carman, M.B. Merickel
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Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU
2013Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) is commonly used for unsupervised image classification in remote sensing applications. Although parallelized approaches were explored, previous works mostly utilized the power of CPU clusters.
Fei Ye, Xuan Shi
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Wireless Channel Scenario Classification Based on ISODATA
2023 5th International Conference on Electronic Engineering and Informatics (EEI), 2023Siyuan Tan +3 more
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Power Load Curve Clustering based on ISODATA
2022 IEEE 7th International Conference on Smart Cloud (SmartCloud), 2022Zhu Li, Xia Yu
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