High-dimensional data clustering algorithm based on stacked-random projection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246918" target="_blank" >RIV/61989100:27240/20:10246918 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-57796-4_38" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-57796-4_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-57796-4_38" target="_blank" >10.1007/978-3-030-57796-4_38</a>
Alternative languages
Result language
angličtina
Original language name
High-dimensional data clustering algorithm based on stacked-random projection
Original language description
This study focuses on high dimensional data, which are characterized by sparsity, redundancy, and high computational complexity. It is impossible to obtain expected results via clustering with traditional algorithms due to the "Curse of Dimensionality". In this study, we propose a Stacked-Random Projection dimensionality reduction framework and a dimensionality reduction evaluation index based on distance preservation. The algorithm uses Stacked-Random Projection to reduce the dimensionality of the high-dimensional data, and then spectral clustering and fast search and find density peak clustering are used to cluster the processed data. The algorithm is validated using two high-dimensional data sets. Experimental results show that this algorithm can improve the performance of clustering algorithm significantly. (C) Springer Nature Switzerland AG 2021.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 1263
ISBN
978-3-030-57795-7
ISSN
2194-5357
e-ISSN
2194-5365
Number of pages
11
Pages from-to
391-401
Publisher name
Springer
Place of publication
Cham
Event location
Victoria
Event date
Aug 31, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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