Clustering Boundary Detecting Algorithm for Each Cluster

DOI

10.2991/icmemtc-16.2016.76How to use a DOI?

Keywords

Clustering Boundary Detecting Algorithm for Each Cluster

Abstract

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 KNN and RKNN named CBDEC(Clustering Boundary Detecting Algorithm for Each Cluster: CBDEC) is proposed. Firstly, the KNN and the RKNN for each object in the data set will be calculated. And the boundary degree of each object will be calculated according to its RKNN value. Then, a concept named Reached Neighbors(RN) is proposed according to the neighbors' relationship between the objects. And an edge will be put between the objects which are satisfied the concept of RN. Many connected undirected graphs will be constituted in this way, and each one of them represents a cluster. Finally, The boundary of the whole data set or each cluster can be detected by boundary degree combined with the boundary percent and the cluster division. The experimental results on many data sets with noises show that CBDEC algorithm can obtain the boundary of the whole data set or each cluster 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  - Kun Wang
AU  - Baozhi Qiu
AU  - Xiangdong Shen
PY  - 2016/04
DA  - 2016/04
TI  - Clustering Boundary Detecting Algorithm for Each Cluster
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 394
EP  - 398
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.76
DO  - 10.2991/icmemtc-16.2016.76
ID  - Wang2016/04
ER  -