TY - JOUR
T1 - A Method for Autonomous Data Partitioning
AU - Gu, Xiaowei
AU - Angelov, Plamen Parvanov
AU - Principe, Jose
N1 - Funding Information:
This work was partially supported by The Royal Society grant IE141329/2014 “Novel Machine Learning Paradigms to address Big Data Streams”.
Funding Information:
This work was partially supported by The Royal Society grant IE141329/2014 “Novel Machine Learning Paradigms to address Big Data Streams”.
Publisher Copyright:
© 2018 Elsevier Inc.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms.
AB - In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms.
KW - Autonomous
KW - Data partitioning
KW - Local modes
KW - Voronoi tessellation
UR - http://www.research.lancs.ac.uk/portal/en/publications/a-method-for-autonomous-data-partitioning(c591d0e2-dce2-4fb6-b366-e1a60abdf5f5).html
UR - http://www.scopus.com/inward/record.url?scp=85047453358&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2018.05.030
DO - 10.1016/j.ins.2018.05.030
M3 - Article
SN - 0020-0255
VL - 460-461
SP - 65
EP - 82
JO - Information Sciences
JF - Information Sciences
ER -