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How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

Overview of attention for article published in Journal of Cheminformatics, January 2016
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Title
How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity
Published in
Journal of Cheminformatics, January 2016
DOI 10.1186/s13321-016-0114-x
Pubmed ID
Authors

Wilmer Leal, Eugenio J. Llanos, Guillermo Restrepo, Carlos F. Suárez, Manuel Elkin Patarroyo

Abstract

Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies. Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets. Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstractFour cluster contrast functions identifying statistically sound clusters within dendrograms considering ties in proximity.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 29%
Researcher 7 17%
Student > Ph. D. Student 6 15%
Student > Doctoral Student 2 5%
Student > Bachelor 2 5%
Other 5 12%
Unknown 7 17%
Readers by discipline Count As %
Chemistry 6 15%
Biochemistry, Genetics and Molecular Biology 6 15%
Agricultural and Biological Sciences 6 15%
Computer Science 5 12%
Medicine and Dentistry 4 10%
Other 4 10%
Unknown 10 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 February 2016.
All research outputs
#3,281,759
of 7,289,383 outputs
Outputs from Journal of Cheminformatics
#247
of 348 outputs
Outputs of similar age
#139,918
of 321,334 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 16 outputs
Altmetric has tracked 7,289,383 research outputs across all sources so far. This one has received more attention than most of these and is in the 52nd percentile.
So far Altmetric has tracked 348 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.