With the ever increasing data, there is a greater need for analyzing and extracting useful and meaningful information out of it. The amount of research being conducted in extracting this information is commendable. Fr...
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An optical modulation format identification technique is proposed based on signal amplitude features and clustering algorithms. Successful classification among five different polarization-multiplexed modulation signal...
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This paper presents Wind Turbine Power Curve (WTPC) modeling based on hybrid fuzzy clustering algorithms and cubic spline. One of the advantages in using fuzzy clustering algorithms is their capability to deal with un...
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clustering in data mining is a supreme step toward organizing data into some meaningful patterns. It plays an extremely crucial role in the entire KDD process, and also as categorizing data is one of the most rudiment...
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This paper performs a clustering algorithm for portfolio investment diversification. The clustering process is applied to choose the preferred assets among hundreds of assets provided in the market under the related f...
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The number of scientific publications published online has increased significantly in tandem with the rise of the Internet. The quantity of online articles has significantly expanded in recent years. As the quantity o...
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clustering a document collection is the current approach to automatically derive underlying document categories. The categorization performance of a document clustering algorithm can be captured by the F-Measure, whic...
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A promising approach to graph clustering is based on the intuitive notion of intra-cluster density vs. inter-cluster sparsity. While both formalizations and algorithms focusing on particular aspects of this rather vag...
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ISBN:
(纸本)3540200649
A promising approach to graph clustering is based on the intuitive notion of intra-cluster density vs. inter-cluster sparsity. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed no conclusive argument on their appropriateness has been given. As a first step towards understanding the consequences of particular conceptions, we conducted an experimental evaluation of graph clustering approaches. By combining proven techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.
A hierarchical lossy image set compression algorithm (HMST α) has recently been proposed for lossy compression of image sets. It was shown that this algorithm performs well when an image set contains well separated c...
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ISBN:
(纸本)9781601321190
A hierarchical lossy image set compression algorithm (HMST α) has recently been proposed for lossy compression of image sets. It was shown that this algorithm performs well when an image set contains well separated clusters of similar images. As a result, if one applies the HMSTα algorithm after a clustering algorithm has been applied, the compression performance depends on the quality of the partition. In this paper, we examine a number of well-known hierarchical clustering methods and cluster validity measures, and their relationships to the compression performance of HMSTα. This relationship can be used as a component in a fully automated image set compression algorithm. We also briefly examine the merit of using different compression schemes depending on the compactness of the cluster in order to reduce computational complexity.
clustering data streams is a challenging problem in mining data streams. Data streams need to be read by a clustering algorithm in a single pass with limited time, and memory whereas they may change over time. Differe...
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