An Anonymous Algorithm for Hierarchical Clustering Based on K-Prototypes

DOI

10.2991/icmmita-16.2016.291How to use a DOI?

Keywords

k-prototypes; clustering; multi-level; information loss

Abstract

By the research on the clustering problem of multiple attributes data processing based on K-Prototypes algorithm, this paper improves distance formula, which can more accurately reflect the differences between tuples. Besides, according to the various demand of privacy preservation, the sensitive value is divided into multiple levels by (KLS, -clustering) - hierarchical anonymous model. The experimental results show that this algorithm is able to achieve highly accurate clustering results. It can also satisfy the requirements of multi-level privacy preservation of sensitive attributes, and effectively reduce the information loss.

Copyright

© 2017, 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)

TY  - CONF
AU  - Yuan-jing Yao
AU  - Yi Sun
PY  - 2017/01
DA  - 2017/01
TI  - An Anonymous Algorithm for Hierarchical Clustering Based on K-Prototypes
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1279
EP  - 1284
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.291
DO  - 10.2991/icmmita-16.2016.291
ID  - Yao2017/01
ER  -