High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245959" target="_blank" >RIV/61989100:27240/20:10245959 - isvavai.cz</a>
Result on the web
<a href="https://www.hindawi.com/journals/wcmc/2020/8881112/" target="_blank" >https://www.hindawi.com/journals/wcmc/2020/8881112/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2020/8881112" target="_blank" >10.1155/2020/8881112</a>

Result language
angličtina
Original language name
High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak
Original language description
This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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