k-means
Incremental k-means clustering algorithms: a review
... cluster. K-means is most popular clustering algorithm which partitioned the data but When the amount of data to be clustered is large and/or when data becomes available incrementally then incremental ...
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A Novel K-Means Based Clustering Algorithm for High Dimensional Data Sets
... Abstract- Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging ...
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The k-means clustering technique: General considerations and implementation in Mathematica
... Self-Organizing Maps (Kohonen, 1982) are an artificial neural network algorithm that aims to extract attributes present in a dataset and transcribe them into an output space of lower dimensionality, while keeping the ...
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DYNAMIC K-MEANS ALGORITHM FOR OPTIMIZED ROUTING IN MOBILE AD HOC NETWORKS
... basic K-means algorithm and proposed clustering scheme in ...dynamic K-means clustering scheme offers considerably lower than the total route error sent for the other scheme ...
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IP2P K-means: an efficient method for data clustering on sensor networks
... Ribas, A.D., Colonna, J.G., Figueiredo, C.M.S., & Nakamura, E.F. (2012). Similarity clustering for data fusion in Wireless Sensor Networks using k-means. The 2012 International Joint Conference on ...
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Infected Fruit Part Detection using K-Means Clustering Segmentation Technique
... purposes. K-means is a typical clustering algorithm (MacQueen, 1967) [15]. K-means is generally used to determine the natural groupings of pixels present in an ...seed-points. ...
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Refining K-means Algorithm by Detecting Superfluous and Oversized Clusters
... (Refining K-means), clustering subsamples of data for a number of times is ...all K center points are found. In [1] (K-means++), a similar approach is ...
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AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
... K-means clustering (MacQueen, 1967) is a method commonly used to automatically partition a dataset into K groups. It proceeds by selecting K initial cluster centers and then iteratively ...
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Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia
... The objective of this study was to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem was that the decision makers such as central government, local government ...
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Clustering of User Behaviour based on Web Log data using Improved K-Means Clustering Algorithm
... In K-Means clustering algorithm randomly choose K data items from X as initial ...improved K-Means clustering algorithm minimizes the total of squares of the ...
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Text Extraction from Live Captured Image with Diversified Background using Edge Based & K-Means Clustering
... The maximum function shown above is the maximum value in the in ima matrix which represents the colored image in order to achieve the maximum value of the content colors where the color values are revealed as a unit ...
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A survey of K means Clustering with modified gradient magnitude region growing technique for lesion segmentation
... The problem of assigning the adaptation rate to adaptive k-means clustering is very similar to the problem of assigning the learning rate to the back propagation algorithm. Both algorithms are based on the ...
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ESTUDO SOBRE OS ALGORITMOS DE CLUSTERIZAÇÃO HIERARCHICAL CLUSTERER E SIMPLE K-MEANS APLICADOS NO AGRUPAMENTO DE PADRÕES SIMILARES
... Simple K-Means and Hierarchical Clusterer data mining algorithms, measuring their efficiency in identifying similar patterns between sub-area, keywords and academic articles, thus generating clusters based ...
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Binarization of MRI with Intensity Inhomogeneity Using K-Means Clustering for Segmenting Hippocampus
... 12 thresholding [6] [7], annealing-based optimal threshold determining method [8] [9], image intensity standardization for correcting acquisition-to-acquisition signal intensity variations [10] [11] [12], homomorphic ...
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Tese de Doutorado ALGORITMO PARA AGRUPAMENTO DE DESCONTINUIDADES EM FAMÍLIAS BASEADO NO MÉTODO FUZZY K-MEANS
... o K-means se baseia, as incertezas não podem ser identificadas e muito menos tratadas, já que esta usa distinções bem definidas para separar os objetos em conjuntos, classificando-os apenas como ...
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Distribuição de subgrupos com base nas respostas fisiológicas em jogadores profissionais de futebol pela técnica K Means Cluster.
... Para análise da distribuição nos grupos formados em relação ao posicionamento em campo foi utilizado o teste Kruskal-Wallis. Os da- dos foram tabulados para que os grupos fossem formados de maneira estatística por ...
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SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS CLUSTERING
... this k-Means is limited to produce only hyper spherical ...with K-means algorithm the optimal solution is difficult to ...of k-means, ...
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Investigation of Internal Validity Measures for K-Means Clustering
... the k-means algorithm often do not best reflect the “natural” structure of the data for any given ...how k-means clusters the same data set with random initialization, but run 100 times, ...
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Parallel K-Means Algorithm on Agricultural Databases
... Talia[1] identified three main strategies in the parallelism used in data mining algorithms as the following: (1) Independent parallelism where each processor accesses to the whole data to operate but do not communicate ...
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Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach
... clusters) that correspond to a specific damage mode. This method allows the differentiation of signals resulting from damage mechanisms [7]. In statistic and data mining, K-means clustering is well-known ...
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