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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Local modes-based free-shape data partitioning
AU - Angelov, Plamen Parvanov
AU - Gu, Xiaowei
PY - 2016/12/9
Y1 - 2016/12/9
N2 - In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.
AB - In this paper, a new data partitioning algorithm, named “local modes-based data partitioning”, is proposed. This algorithm is entirely data-driven and free from any user input and prior assumptions. It automatically derives the modes of the empirically observed density of the data samples and results in forming parameter-free data clouds. The identified focal points resemble Voronoi tessellations. The proposed algorithm has two versions, namely, offline and evolving. The two versions are both able to work separately and start “from scratch”, they can also perform a hybrid. Numerical experiments demonstrate the validity of the proposed algorithm as a fully autonomous partitioning technique, and achieve better performance compared with alternative algorithms.
KW - data partitioning
KW - evolving clustering
KW - parameter-free
KW - data cloud
KW - data- driven
U2 - 10.1109/SSCI.2016.7850117
DO - 10.1109/SSCI.2016.7850117
M3 - Conference contribution/Paper
SN - 9781509042418
BT - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PB - IEEE
T2 - IEEE SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -