Misc.

Peer-reviewed
Jul 1, 2017

ART-2b: Adapted ART-2a for large scale data clustering on PM2.5 mass spectra

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
  • Nat Pavasant
    • ,
  • Hiroshi Furutani
    • ,
  • Masayuki Numao
    • ,
  • Ken Ichi Fukui
Volume
2018-January
Number
First page
4813
Last page
4815
DOI
10.1109/BigData.2017.8258551
© 2017 IEEE. ART-2a has been shown to be effective against stream data clustering with unknown number of cluster in nature. As data grows, ART-2a running time become a major problem. We proposed a new algorithm, ART-2b, whose runtime performance is linear to the number of input instances, while still maintaining similar clustering result to ART-2a.
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DOI
https://doi.org/10.1109/BigData.2017.8258551
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047722589&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85047722589&origin=inward
ID information
  • DOI : 10.1109/BigData.2017.8258551
  • SCOPUS ID : 85047722589
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