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EnsCat: clustering of categorical data via ensembling

Overview of attention for article published in BMC Bioinformatics, September 2016
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Title
EnsCat: clustering of categorical data via ensembling
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1245-9
Pubmed ID
Authors

Bertrand S. Clarke, Saeid Amiri, Jennifer L. Clarke

Abstract

Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 4%
France 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 22%
Student > Ph. D. Student 5 22%
Professor > Associate Professor 3 13%
Student > Doctoral Student 2 9%
Lecturer 2 9%
Other 4 17%
Unknown 2 9%
Readers by discipline Count As %
Mathematics 5 22%
Computer Science 5 22%
Biochemistry, Genetics and Molecular Biology 2 9%
Agricultural and Biological Sciences 2 9%
Psychology 1 4%
Other 2 9%
Unknown 6 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 September 2016.
All research outputs
#15,329,366
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#5,159
of 7,418 outputs
Outputs of similar age
#195,560
of 323,144 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 120 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.