patternbased spectral clustering methods in directed weighted network (DWN) have significant applications in many domains, including computer engineering, E-commerce and economics. In this paper, we compile several o...
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ISBN:
(纸本)9781538677353
patternbased spectral clustering methods in directed weighted network (DWN) have significant applications in many domains, including computer engineering, E-commerce and economics. In this paper, we compile several of the state of the art algorithms from the point of view of clustering quality over some existing benchmark datasets. Experimental results show that, it is necessary to propose a more common patternbased spectral clustering method in DWN.
To find closeness between two data points, traditional distance based closeness measurement calculates distance between two data points. However, it fails to capture behaviour of data series. Behaviour of data series ...
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To find closeness between two data points, traditional distance based closeness measurement calculates distance between two data points. However, it fails to capture behaviour of data series. Behaviour of data series can be captured by association and disassociation between patterns of data points. This can reflect closeness between them. The same concept can be applied to find association between text documents. Using this philosophy, this paper proposes a novel approach of document association based on context similarity coefficient (CSC). CSC based document association helps to capture contextual relationship between documents. Experiments conducted on standard datasets such as Reuters-21578 and RCV1 show that CSC successfully finds closeness between the documents. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
To find closeness between two data points, traditional distance based closeness measurement calculates distance between two data points. However, it fails to capture behaviour of data series. Behaviour of data series ...
详细信息
To find closeness between two data points, traditional distance based closeness measurement calculates distance between two data points. However, it fails to capture behaviour of data series. Behaviour of data series can be captured by association and disassociation between patterns of data points. This can reflect closeness between them. The same concept can be applied to find association between text documents. Using this philosophy, this paper proposes a novel approach of document association based on context similarity coe_cient (CSC). CSC based document association helps to capture contextual relationship between documents. Experiments conducted on standard datasets such as Reuters-21578 and RCV1 show that CSC successfully finds closeness between the documents.
One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system t...
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ISBN:
(纸本)9781479913091;9781479913107
One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.
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