In the past years, microarray technologies have become a central tool in biological research. The extraction or identification of gene groups with similar expression pattern plays an important role in the analysis of ...
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Reflection seismic data interpretation is an applied method in oil and gas exploration industry. Interpretation of seismic facies could help understanding complexity of internal stratigraphic geometries of complex seq...
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clustering is a common technique for statistical data analysis, clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets according to ...
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The use of clustering for developing a description of a software system's architecture is fairly recent. Thus there is a need to evaluate various clustering algorithms and identify the ones which are expected to g...
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This paper is oriented into the text document retrieval area. The aim of the paper is to compare two soft document clustering methods by using neural networks, the modification of KMART and the nonlinear Hebbian neura...
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This study addresses the line detection problem in digital images containing Persian text, which presents unique challenges due to the language's specific structural characteristics. The challenges include line de...
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In modern applications, clustering algorithms have been emerged learning aid to generate and analyze the huge volumes of data. The foremost clustering objective is to classify same type of data has been grouped with i...
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As an important part of electronic intelligence (ELINT) and electronic support measurement (ESM) systems, radar signal sorting directly affects the performance of electronic reconnaissance equipment and is a key techn...
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We consider efficient communication schemes based on both network-supported and application-level multicast techniques for content-based publication-subscription systems. We show that the communication costs depend he...
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We consider efficient communication schemes based on both network-supported and application-level multicast techniques for content-based publication-subscription systems. We show that the communication costs depend heavily on the network configurations, distribution of publications and subscriptions. We devise new algorithms and adapt existing partitional data clustering algorithms. These algorithms can be used to determine multicast groups with as much commonality as possible, based on the totality of subscribers' interests. They perform well in the context of highly heterogeneous subscriptions, and they also scale well. An efficiency of 60% to 80% with respect to the ideal solution can be achieved with a small number of multicast groups (less than 100 in our experiments). Some of these same concepts can be applied to match publications to subscribers in real-time, and also to determine dynamically whether to unicast, multicast or broadcast information about the events over the network to the matched subscribers. We demonstrate the quality of our algorithms via simulation experiments.
In densely deployed wireless sensor networks, spatial data correlations are introduced by the observations of multiple spatially proximal sensor nodes on a same phenomenon or event. These correlations bring significan...
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In densely deployed wireless sensor networks, spatial data correlations are introduced by the observations of multiple spatially proximal sensor nodes on a same phenomenon or event. These correlations bring significant potential advantages for the development of efficient strategies for reducing energy consumption. In this paper, spatial data correlations are exploited to group sensor nodes into clusters of high data aggregation efficiency. We define the problem of selecting the set of cluster heads as the weighted connected dominating set problem. Then we develop a set of centralized and distributed algorithms to select the cluster heads. Simulation results demonstrate the effectiveness and efficiency of the designed algorithms.
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