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adaptive k-means algorithm

Image segmentation based on adaptive K-means algorithm

Image segmentation based on adaptive K-means algorithm

... segmentation algorithm mainly in- cludes the segmentation method based on the threshold value [1], the segmentation method based on the edge [2] and the segmentation method based on the region ...proved ...

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An efficient document clustering by using adaptive k-means clustering algorithm

An efficient document clustering by using adaptive k-means clustering algorithm

... of adaptive K-Means algorithm whereEuclidean and Cosine distance measures are employed for finding the distance between terms in text ...

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Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition

Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition

... propose adaptive K-means algorithm upon the principal component analysis PCA feature extraction to pattern recognition by using a neural network ...model. Adaptive k-means ...

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Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

Optimization of Biclustering Algorithm Based on Greedy Randomized Adaptive Search Procedure

... biclustering algorithm is a heuristic method [9,10,11], which requires much less computation than the exhaustive search method, and δ -bicluster can be found in a reasonable time ...biclustering algorithm ...

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A Survey on Clustering Algorithms for Image Segmentation

A Survey on Clustering Algorithms for Image Segmentation

... MKM algorithm is that it obligates the members of the center with the largest fitness value to become a member of the center with the smallest fitness value , even though, the center with the smallest fitness ...

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Adaptive Distributed Intrusion Detection using Hybrid K means SVM Algorithm

Adaptive Distributed Intrusion Detection using Hybrid K means SVM Algorithm

... Assuring secure and reliable operation of networks has become a priority research area these days because of ever growing dependency on network technology. Intrusion detection systems (IDS) are used as the last line of ...

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Detection of Cataract by Statistical Features and Classification

Detection of Cataract by Statistical Features and Classification

... by Adaptive Histogram ...for K-means and ANFIS ...for K-means clustering produced good ...the K-means and ANFIS are ...the K-means and ANFIS classifier ...

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Adaptive K-Means Clustering Techniques For Data Clustering

Adaptive K-Means Clustering Techniques For Data Clustering

... clusters. K-means algorithm dependence on partition- based clustering technique is popular and widely used and applied to a variety of ...domains. K-means clustering results are ...

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AN ADAPTIVE MEAN-SHIFT ALGORITHM FOR MRI BRAIN SEGMENTATION

AN ADAPTIVE MEAN-SHIFT ALGORITHM FOR MRI BRAIN SEGMENTATION

... K-means algorithm is under the category of Squared Error-Based Clustering (Vector Quantization) and it is also under the category of crisp clustering or hard ...clustering. K-means ...

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An Efficient Global K-means Clustering Algorithm

An Efficient Global K-means Clustering Algorithm

... new algorithm can significantly reduce the computational time without affecting the performance of the global K- means ...global K-means algorithm outperforms the global ...

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On the Consequence of Variation Measure in K- Modes Clustering Algorithm

On the Consequence of Variation Measure in K- Modes Clustering Algorithm

... the k-mode algorithm with the new Variation measure and the original k-mode algorithm on the data ...the k-mode algorithm using the new Variation ...the algorithm stops ...

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An improved density based k Means algorithm

An improved density based k Means algorithm

... hierarchical. K- means clustering algorithm is a well-known partitioning type of clustering algorithm used across different domains due to its simplicity (Abubaker and Ashour, 2013), it is ...

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Implementing & Improvisation of K-means Clustering Algorithm

Implementing & Improvisation of K-means Clustering Algorithm

... basic K-mean clustering algorithm, clusters are fully dependent on the selection of the initial clusters ...centroids. K data elements are selected as initial centers; then distances of all data ...

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Segmentation of Medical Images using Adaptively Regularized Kernel based Fuzzy C Means Clustering

Segmentation of Medical Images using Adaptively Regularized Kernel based Fuzzy C Means Clustering

... as k means, fuzzy C-means, Gaussian Mixture and Density Estimation) and spectral ...C- means (FCM) clustering and spectral clustering algorithms are research ...

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K-MEANS Clustering with a Covariance Matrix

K-MEANS Clustering with a Covariance Matrix

... for k-means method many researchers propsed metric based imporvement ...harmaonic means as a distance metric in k-means algorithm that to handle ...the k-means ...

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Segmentation of MR images for Tumor extraction by using clustering algorithms

Segmentation of MR images for Tumor extraction by using clustering algorithms

... The quantization of the feature space is performed by masking the lower ‘m’ bits of the feature value. The quantized output will result in the common intensity values for more than one feature vector. In the process of ...

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Plant Operation Working Condition of the Optimal Combination of External Research Division

Plant Operation Working Condition of the Optimal Combination of External Research Division

... If k is too small in mining, then the scope of each working interval is relatively large, and it is easy to lead to the best working range too large in mining that it makes no significance of guiding the operation ...

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A Neighborhood Probability Based Agglomerative Clustering for Test Case Prioritization in Regression Testing Anju Bala

A Neighborhood Probability Based Agglomerative Clustering for Test Case Prioritization in Regression Testing Anju Bala

... In this paper, main intention is test case prioritization of test cases such that the testing endeavor reduces significantly while the code coverage remains more or less same. This is accomplished by using clustering ...

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Iteration Reduction K Means Clustering Algorithm

Iteration Reduction K Means Clustering Algorithm

... Enhancing K-means Clustering Algorithm with Improved Initial Center [7], main aim is to reduce the initial centroid for K Mean ...clustering algorithm results of K Means ...

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An intelligent System for Diagnosing Schizophrenia and Bipolar Disorder based on MLNN and RBF M. I. Elgohary 1, Tamer. A. Alzohairy2 , Amir. M. Eissa 1, sally.Eldeghaidy3 , Hussein. M *1

An intelligent System for Diagnosing Schizophrenia and Bipolar Disorder based on MLNN and RBF M. I. Elgohary 1, Tamer. A. Alzohairy2 , Amir. M. Eissa 1, sally.Eldeghaidy3 , Hussein. M *1

... Training RBF neural network consists of determining the location of centers and widths for the hidden layer and the weights of the output layer. It is trained using a two-phase approach: in the first phase, unsupervised ...

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