In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors ...
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In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.
It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm tha...
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It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks. (c) 2006 Elsevier Inc. All rights reserved.
Optical disc drives are subject to various disturbances and faults. A special type of fault is the so-called disc defect. In this paper we present an approach for disc defect classification. It is based on hierarchica...
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ISBN:
(纸本)0780383354
Optical disc drives are subject to various disturbances and faults. A special type of fault is the so-called disc defect. In this paper we present an approach for disc defect classification. It is based on hierarchicalclustering of measured signals that are affected by disc defects. The time-series are mapped into a feature space after which the feature vectors are clustered in a hierarchical fashion. Finally, signals are fitted onto the clusters to obtain single representations for each fault class. The resulting class descriptions can then be used for (on-line) classification of new disc defects. The approach is evaluated by applying it to a set of test data.
Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchicalclustering can be...
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Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchicalclustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality.
Self-generating neural networks (SGNNs) have been in the spotlight of the fields of neural networks algorithm research for the sake of their efficiency. In practice, this neural network is implemented as a self-genera...
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Self-generating neural networks (SGNNs) have been in the spotlight of the fields of neural networks algorithm research for the sake of their efficiency. In practice, this neural network is implemented as a self-generating neural tree (SGNT) which is based on a hierarchical clustering algorithm. In this paper, we present the superior performance of the SGNT when it is applied to character recognition problems. Basically, the SGNT algorithm is generated as a kind of competitive learning algorithm. Therefore, it is natural to have a competent performance at the area of clustering or classification. However, our experimental results show that the SGNN method is very efficient to solve even pattern recognition problems, especially when they include a noisy signal problem. (C) 2003 Elsevier Ltd. All rights reserved.
Traditional hierarchical clustering algorithms require the calculation of a dissimilarity matrix which is mapped to a binary tree or 'dendogram' based upon some predetermined criterion. Although 'optimally...
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ISBN:
(纸本)0819428930
Traditional hierarchical clustering algorithms require the calculation of a dissimilarity matrix which is mapped to a binary tree or 'dendogram' based upon some predetermined criterion. Although 'optimally efficient' algorithms requiring O(N-2) time and O(N) storage are known for several clustering methods, with few exceptions these algorithms are relatively inefficient in practice as many pairwise distances are measured which are not necessary for generation of the binary tree. We describe here a novel 'almost single link' algorithm which is efficient both theoretically and in practice, and which can be extended to provide fast (albeit suboptimal) algorithms for centroid, median and single link clustering of large data sets. Generalisation to other related clustering methods is expected to be straightforward. Our algorithm also suggests a fairly efficient method for generating minimal spanning trees. In performing the segmentation we employ a particular representation of the binary tree which simplifies the task of manual investigation of the hierarchy. A customised graphical user interface including a two-dimensional scatter plot, a visual display of the dendogram, and a false colour image with overlayed clusters makes the clustering procedure a highly interactive one. By suggesting, for each of the clustering methods, possible criteria which might be useful for extracting relevant clusters from the tree information, we are able to fully automate the cluster selection procedure and thereby further reduce the effort required to segment an image. The algorithms described have been transcribed into C code and combined into a single package, the "hierarchical Agglomerative Clusterer" (HAC), which has been applied to the analysis of hyperspectral image data of various forest and desert scenes acquired by the HYDICE sensor. The analyses were performed on a 266 Mhz Pentium PC platform running Windows NT 4.0. Typical segmentation times for the fastest algorithm ranged fro
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