具有隐私保护功能的知识迁移聚类算法

陈爱国; 王士同

doi:10.11999/JEIT150645

具有隐私保护功能的知识迁移聚类算法

doi: 10.11999/JEIT150645

陈爱国¹,
王士同¹

基金项目:

国家自然科学基金(61272210)，江苏省杰出青年基金(BK20140001)，江苏省自然科学基金(BK20130155)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-06-01
- 修回日期: 2015-11-02
- 刊出日期: 2016-03-19

Knowledge Transfer Clustering Algorithm with Privacy Protection

CHEN Aiguo¹,
WANG Shitong¹

Funds:

The National Natural Science Foundation of China (61272210), Jiangsu Province Outstanding Youth Fund (BK20140001), Natural Science Foundation of Jiangsu Province (BK20130155)

摘要

摘要: 传统聚类算法在数据量不足或数据被污染的场景下聚类效果较差，针对此问题，在经典模糊C均值(FCM)技术的基础上，该文提出融合历史类中心和历史隶属度两类知识迁移机制的聚类算法。该算法通过有效利用历史数据中总结得到的辅助知识来指导当前由于数据不足或数据污染带来的聚类困难问题，从而提高聚类效果。同时，由于该算法仅利用历史数据的类中心和隶属度，对历史数据具有隐私保护的优点。通过在模拟数据集和真实数据集上的仿真实验，证明了该算法的有效性。
- 知识迁移 /
- 隐私保护 /
- 聚类算法 /
- 模糊C均值(FCM)
Abstract: For the traditional clustering algorithms efficiency problems in situations of insufficient datasets or datasets with noises, a Knowledge Transfer Clustering Algorithm with Privacy Protection (KTCAPP) is proposed based on the classical Fuzzy C-Means (FCM) technology by leveraging two kinds of knowledge which are the historical class center and the historical class membership. The performance of KTCAPP is enhanced by using auxiliary knowledge from history datasets to guide the current clustering task with insufficient datasets or datasets with noises. In addition, KTCAPP is of good capability of privacy protection because the algorithm only uses the historical class center and the historical class membership which do not expose the raw data. Experiment results show the proposed algorithm is efficient.
- Knowledge transfer /
- Privacy protection /
- Clustering algorithm /
- Fuzzy C-Means (FCM)

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参考文献(29)

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