@inproceedings{f1004a559d9a41e986ed9ac0224cafea,
title = "Local modes-based free-shape data partitioning",
abstract = "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.",
keywords = "data cloud, data partitioning, data- driven, evolving clustering, parameterfree",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",
year = "2017",
month = feb,
day = "13",
doi = "10.1109/SSCI.2016.7850117",
language = "English",
isbn = "9781509042418",
series = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",
publisher = "IEEE Press",
booktitle = "2016 IEEE Symposium Series on Computational Intelligence (SSCI)",
address = "United States of America",
}