Boundary Detecting Algorithm for Each Cluster based on DBSCAN

DOI

10.2991/icamcs-16.2016.62How to use a DOI?

Keywords

Clustering, Cluster numbers, Boundary, Point density, Border degree

Abstract

Detecting Detecting the boundary of each cluster in a data set is a tough problem for many existed boundary detecting algorithms. In order to solve that problem, a clustering boundary detecting algorithm based on DBSCAN named BDAEC(Boundary Detecting Algorithm for Each Cluster based on DBSCAN: BDAEC) is proposed . Firstly, according to the core point percent and the density value of each data object, all the core points are extracted by this algorithm from the data set. Then, many connected undirected graphs will be constituted by these core points. And the cluster numbers of the data set can be known by those connected undirected graphs for each one of them represents a cluster. Finally, Eps field will be diveded into two fields: the positive field and the negative field. And the boundary of each cluster or the whole data set can be detected by the distribution characteristics of the data objects which are located in the positive field and negative field of the given data object. The experimental results on many data sets with noise show that BDAEC algorithm can obtain the numbers and the boundaries of the clusters with different size or shapes effectively.

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/).

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TY  - CONF
AU  - Yarui Guo
AU  - Jingzhe Wang
AU  - Kun Wang
PY  - 2016/06
DA  - 2016/06
TI  - Boundary Detecting Algorithm for Each Cluster based on DBSCAN
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 293
EP  - 297
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.62
DO  - 10.2991/icamcs-16.2016.62
ID  - Guo2016/06
ER  -