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Search results for: news clustering
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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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Categorization of Wikipedia articles with spectral clustering
PublicationAbstract. The article reports application of clustering algorithms for creating hierarchical groups withinWikipedia articles.We evaluate three spectral clustering algorithms based on datasets constructed with usage ofWikipedia categories. Selected algorithm has been implemented in the system that categorize Wikipedia search results in the fly.
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Clustering Context Items into User Trust Levels
PublicationAn innovative trust-based security model for Internet systems is proposed. The TCoRBAC model operates on user profiles built on the history of user with system interaction in conjunction with multi-dimensional context information. There is proposed a method of transforming the high number of possible context value variants into several user trust levels. The transformation implements Hierarchical Agglomerative Clustering strategy....
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Clustering Bathymetric Data for Electronic Navigational Charts
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Agent-Based Non-distributed and Distributed Clustering
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Weighted Clustering for Bees Detection on Video Images
PublicationThis work describes a bee detection system to monitor bee colony conditions. The detection process on video images has been divided into 3 stages: determining the regions of interest (ROI) for a given frame, scanning the frame in ROI areas using the DNN-CNN classifier, in order to obtain a confidence of bee occurrence in each window in any position and any scale, and form one detection window from a cloud of windows provided by...
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Ontology clustering by directions algorithm to expand ontology queries
PublicationThis paper concerns formulating ontology queries. It describes existing languages in which ontologies can be queried. It focuses on languages which are intended to be easily understood by users who are willing to retrieve information from ontologies. Such a language can be, for example, a type of controlled natural language (CNL). In this paper a novel algorithm called Ontology Clustering by Directions is presented. The algorithm...
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Interactive Query Expansion with the Use of Clustering by Directions Algorithm
PublicationThis paper concerns Clustering by Directions algorithm. The algorithm introduces a novel approach to interactive query expansion. It is designed to support users of search engines in forming web search queries. When a user executes a query, the algorithm shows potential directions in which the search can be continued. This paper describes the algorithm and it presents an enhancement which reduces the computational complexity of...
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External Validation Measures for Nested Clustering of Text Documents
PublicationAbstract. This article handles the problem of validating the results of nested (as opposed to "flat") clusterings. It shows that standard external validation indices used for partitioning clustering validation, like Rand statistics, Hubert Γ statistic or F-measure are not applicable in nested clustering cases. Additionally to the work, where F-measure was adopted to hierarchical classification as hF-measure, here some methods to...
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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...