clustering algorithms belong to an area of research that has many practical uses. Over the years, many different clustering algorithms have been proposed. Of these, the majority that are in common use today tend to be...
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clustering Search (*CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep r...
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In recent years, the uncertain data stream which is related in many real applications attracts more and more attention of researchers. As one aspect of uncertain character, existence-uncertainty can affect the cluster...
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As the core step of clustering analysis, the results of distance measurements will influence the clustering accuracy. The existing measurements are mostly based on the information about cluster features. However, the ...
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Size-Constrained clustering tries to solve the problem that how to classify dataset into groups based on each document's similarity with additional requirement which each group size is within a fixed range. By far...
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This paper discusses a novel approach to associate and rene aircraft track data from multiple radar sites. The approach provides enhanced aircraft track accuracy and time synchronization that is compatible with modern...
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This paper adopts statistical learning theory and optimization theory to the analysis of the algorithm theory, probe into its theoretical foundation. The existing theoretical analysis on the basis of the establishment...
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The key of knowledge resources polymerization is to cluster the knowledge resources exist in document form, knowledge document set is divided into some clusters, require the similarity of document content within the s...
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The clustering algorithm is a popular lightning data processing method. Traditional density-based algorithm can't function without input of initial parameter Min-ρ or neighborhood radius ϵ. Besides, in most regar...
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In the recent years, major CPU designers have shifted from ramping up clock speeds to add on-chip Multi-Core processors. algorithms and applications must be tuned to allow Multi-Core processors to exploit their inhere...
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ISBN:
(纸本)9789380043579
In the recent years, major CPU designers have shifted from ramping up clock speeds to add on-chip Multi-Core processors. algorithms and applications must be tuned to allow Multi-Core processors to exploit their inherent parallelism. An experiment is carried out with data mining (DM) algorithms, to explore the potential of Quad-core hardware architecture with OpenMP API (Application Programming Interface). The concept of memory mapped files is widely supported by most of the modern operating systems. Performance of memory mapped files on Multi-Core processor is also explored. In this experiment, a popular clustering algorithm k-means with serial versions and parallel versions are used. The later is experimented with static and dynamic threads of OpenMP. Memory utilization of k-means algorithm is also analyzed. Experimental results with both simulated and real data demonstrate the scalability of our implementation and effective utilization of parallel hardware, which is beneficial to data mining (DM) problems, which involves large data sets. The capacity of RAM (Random Access Memory) affects the mmap() performance.
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