Local modes-based free-shape data partitioning

Angelov, Plamen Parvanov and Gu, Xiaowei (2016) Local modes-based free-shape data partitioning. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) :. IEEE, GRC. ISBN 9781509042418

[thumbnail of Datacloud-localmode_v3]
PDF (Datacloud-localmode_v3)
Datacloud_localmode_v3.pdf - Accepted Version

Download (910kB)


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.

Item Type:
Contribution in Book/Report/Proceedings
?? data partitioningevolving clusteringparameter-freedata clouddata- driven ??
ID Code:
Deposited By:
Deposited On:
13 May 2017 03:22
Last Modified:
20 Apr 2024 00:23