Results 161 to 170 of about 2,173 (213)

A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
In this paper the convergence of a class of clustering procedures, popularly known as the fuzzy ISODATA algorithms, is established. The theory of Zangwill is used to prove that arbitrary sequences generated by these (Picard iteration) procedures always terminates at a local minimum, or at worst, always contains a subsequence which converges to a local ...
James C Bezdek
exaly   +3 more sources

Pyramid linking is a special case of ISODATA

IEEE Transactions on Systems, Man, and Cybernetics, 1983
It is shown that the `pyramid linking' method of image segmentation can be regarded as a special case of the ISODATA clustering algorithm and hence is guaranteed to converge.
Simon Kasif
exaly   +2 more sources

Supervising ISODATA with an information theoretic stopping rule

Pattern Recognition, 1990
Abstract New biomedical imaging modalities, such as Magnetic Resonance Imaging (MRI), provide fertile multidimensional environments for the automatic identification of biological soft tissues, but lack the a priori information required to appropriately train supervised classifiers.
Charles S. Carman, Michael B. Merickel
exaly   +2 more sources

Binary tree design using fuzzy isodata

Pattern Recognition Letters, 1986
A procedure for designing a fuzzy binary decision tree using unlabeled samples is developed. At each node, the data is split into two dissimilar groups using the fuzzy Isodata algorithm. All the available features are used while clustering. Then, the best feature among these available features is selected based on some separation index.
B. Bharathi Devi, V. V. S. Sarma
openaire   +2 more sources

Feature selection based on sensitivity analysis of fuzzy ISODATA

Neurocomputing, 2012
A 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, Zhimin Zhao, Ying-Xin Li, Yuanyuan Li   +3 more
exaly   +2 more sources

A dissimilarity criterion for sequential fuzzy isodata

Fuzzy Sets and Systems, 1987
A criterion for dissimilarity is provided so that new prototypes may be discovered when sample feature vectors are received sequentially in time, as input to the fuzzy ISODATA process.
exaly   +3 more sources

An improved ISODATA algorithm for hyperspectral image classification

2014 7th International Congress on Image and Signal Processing, 2014
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this
Qingli Li
exaly   +2 more sources

Parallelizing ISODATA Algorithm for Unsupervised Image Classification on GPU

2013
Iterative 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.
Xuan Shi
exaly   +2 more sources

An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset

Energy and Buildings, 2017
An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn\u27t separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement.
Mingchao Li, Jonathan Shi
exaly   +2 more sources

Analysis of Landsat 5 TM data of Malaysian land covers using ISODATA clustering technique [PDF]

, 2012
This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification. ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal ...
Asmala Ahmad, Suliadi Firdaus Sufahani
exaly   +2 more sources