Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

Multi Angle Analysis of The Existing Clustering Algorithms

Authors
Jinzhen Ping, Qian Wang, Lili Yu, XueFang Wu
Corresponding Author
Jinzhen Ping
Available Online February 2016.
DOI
10.2991/iccsae-15.2016.77How to use a DOI?
Keywords
data mining; clustering; algorithm
Abstract

Data mining clustering is a broad research field. It is used to partition the data set of clusters. Different clustering methods use different similarity definition and technology. Several popular clustering algorithms are analyzed from three different perspectives: the clustering criterion, clustering algorithm and frame representation. Furthermore, some new construction algorithm, mixed or generalization of some algorithm were introduced. As a result of the analysis of several points of view, it can be covered and distinguished from most existing algorithms. It is based on self tuning algorithm and clustering benchmark.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
Series
Advances in Computer Science Research
Publication Date
February 2016
ISBN
10.2991/iccsae-15.2016.77
ISSN
2352-538X
DOI
10.2991/iccsae-15.2016.77How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jinzhen Ping
AU  - Qian Wang
AU  - Lili Yu
AU  - XueFang Wu
PY  - 2016/02
DA  - 2016/02
TI  - Multi Angle Analysis of The Existing Clustering Algorithms
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 404
EP  - 407
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.77
DO  - 10.2991/iccsae-15.2016.77
ID  - Ping2016/02
ER  -