Structural measures of clustering quality on graph samples

Zhang, Jianpeng; Pei, Yulong; Fletcher, George H. L.; Pechenizkiy, Mykola

doi:10.1109/asonam.2016.7752256

Cited by 9 publications

(11 citation statements)

References 8 publications

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“…Two metrics have been included in the methodology: entropy and purity , which indicates how different regions are distributed inside the same cluster [42].…”

Section: Metrics For Data Evaluationmentioning

confidence: 99%

Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

Rosati¹,

Lupaşcu

Tegolo

2018

IET Computer Vision

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The Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome-scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al., which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and local approach, voxels were clustered using hierarchical and partitioning clustering techniques. Cluster analysis and a validation method based on entropy and purity were performed. They analyse the resulting clusters of the mouse brain for different number of groups and compared them with a classically-defined anatomical reference atlas. The high degree of correspondence between clusters and anatomical regions highlights how gene expression patterns with a low Pearson correlation coefficient between sagittal and coronal sections can accurately identify different neuroanatomical regions.

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“…Two metrics have been included in the methodology: entropy and purity , which indicates how different regions are distributed inside the same cluster [42].…”

Section: Metrics For Data Evaluationmentioning

confidence: 99%

Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

Rosati¹,

Lupaşcu

Tegolo

2018

IET Computer Vision

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“…For the synthetic graphs in which the ground-truth is already known, we utilize supervised quality metrics (i.e., δ-precison and δ-recall) proposed in [30] and normalized mutual information (NMI) [16] to measure the clustering results by using the ground-truth information. Higher value of δ-precision means that the obtained clusters in Ω are more precisely representative of the ground truth in G while higher value of δ-recall indicates the ground truth in G are more successfully covered by the obtained clusters in Ω.…”

Section: Quality Experimentsmentioning

confidence: 99%

A bounded-size clustering algorithm on fully-dynamic streaming graphs

Zhang

Pei

Fletcher

et al. 2018

IDA

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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“…Maiya et al [19] argue that the representativeness should be consistent with the properties that need to be preserved. Most of sampling approaches only assess the extent to which the sampled subgraph is representative of explicit/simple topological properties (e.g., the degree distribution), but are not adequate for retaining the intrinsic property, i.e., clustering structure, which is prevailing in many real-world graphs under study today [1,10,33,35] .…”

Section: Introductionmentioning

confidence: 99%

Cluster-preserving sampling from fully-dynamic streaming graphs

Zhang

Zhu

Pei

et al. 2019

Information Sciences

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Structural measures of clustering quality on graph samples

Cited by 9 publications

References 8 publications

Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

A bounded-size clustering algorithm on fully-dynamic streaming graphs

Cluster-preserving sampling from fully-dynamic streaming graphs

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