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Search results for: K-MEANS CLUSTERING
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K-means clustering for SAT-AIS data analysis
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Breast Cancer Heterogeneity Investigation: Multiple k-Means Clustering Approach
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Increasing K-Means Clustering Algorithm Effectivity for Using in Source Code Plagiarism Detection
PublicationThe problem of plagiarism is becoming increasingly more significant with the growth of Internet technologies and the availability of information resources. Many tools have been successfully developed to detect plagiarisms in textual documents, but the situation is more complicated in the field of plagiarism of source codes, where the problem is equally serious. At present, there are no complex tools available to detect plagiarism...
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0-step K-means for clustering Wikipedia search results
PublicationThis article describes an improvement for K-means algorithm and its application in the form of a system that clusters search results retrieved from Wikipedia. The proposed algorithm eliminates K-means isadvantages and allows one to create a cluster hierarchy. The main contributions of this paper include the ollowing: (1) The concept of an improved K-means algorithm and its application for hierarchical clustering....
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Kernel-Based Fuzzy C-Means Clustering Algorithm for RBF Network Initialization
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Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms
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The chapter analyses the K-Means algorithm in its parallel setting. We provide detailed description of the algorithm as well as the way we paralellize the computations. We identified complexity of the particular steps of the algorithm that allows us to build the algorithm model in MERPSYS system. The simulations with the MERPSYS have been performed for different size of the data as well as for different number of the processors used for the computations. The results we got using the model have been compared to the results obtained from real computational environment.
PublicationThe chapter analyses the K-Means algorithm in its parallel setting. We provide detailed description of the algorithm as well as the way we paralellize the computations. We identified complexity of the particular steps of the algorithm that allows us to build the algorithm model in MERPSYS system. The simulations with the MERPSYS have been performed for different size of the data as well as for different number of the processors used...
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Categorization of Cloud Workload Types with Clustering
PublicationThe paper presents a new classification schema of IaaS cloud workloads types, based on the functional characteristics. We show the results of an experiment of automatic categorization performed with different benchmarks that represent particular workload types. Monitoring of resource utilization allowed us to construct workload models that can be processed with machine learning algorithms. The direct connection between the functional...
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A Clustering-Based Methodology for Selection of Fault Tolerance Techniques
PublicationDevelopment of dependable applications requires selection of appropriate fault tolerance techniques that balance efficiency in fault handling and resulting consequences, such as increased development cost or performance degradation. This paper describes an advisory system that recommends fault tolerance techniques considering specified development and runtime application attributes. In the selection process, we use the K-means...
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Image Segmentation of MRI image for Brain Tumor Detection
Publicationthis research work presents a new technique for brain tumor detection by the combination of Watershed algorithm with Fuzzy K-means and Fuzzy C-means (KIFCM) clustering. The MATLAB based proposed simulation model is used to improve the computational simplicity, noise sensitivities, and accuracy rate of segmentation, detection and extraction from MR...