An Anonymous Algorithm for Hierarchical Clustering Based on K-Prototypes
- https://doi.org/10.2991/icmmita-16.2016.291How to use a DOI?
- k-prototypes; clustering; multi-level; information loss
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/icmmita-16.2016.291 ID - Yao2017/01 ER -