GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10431050" target="_blank" >RIV/00216208:11320/21:10431050 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/book/10.1007/978-3-030-85665-6" target="_blank" >https://link.springer.com/book/10.1007/978-3-030-85665-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-85665-6_36" target="_blank" >10.1007/978-3-030-85665-6_36</a>
Alternative languages
Result language
angličtina
Original language name
GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis
Original language description
Hierarchical clustering is a common tool for simplification, exploration, and analysis of datasets in many areas of research. For data originating in flow cytometry, a specific variant of agglomerative clustering based Mahalanobis-average linkage has been shown to produce results better than the common linkages. However, the high complexity of computing the distance limits the applicability of the algorithm to datasets obtained from current equipment. We propose an optimized, GPU-accelerated open-source implementation of the Mahalanobis-average hierarchical clustering that improves the algorithm performance by over two orders of magnitude, thus allowing it to scale to the large datasets. We provide a detailed analysis of the optimizations and collected experimental results that are also portable to other hierarchical clustering algorithms; and demonstrate the use on realistic high-dimensional datasets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Euro-Par 2021: Parallel Processing
ISBN
978-3-030-85665-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
16
Pages from-to
580-595
Publisher name
Springer
Place of publication
Neuveden
Event location
Lisbon, Portugal
Event date
Sep 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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