Knowledge transfer clustering algorithm with privacy protection

被引：0

作者：

Chen A. ^{[1
]}

Wang S. ^{[1
]}

机构：

[1] School of Digital Media, Jiangnan University, Wuxi

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2016年 / 38卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Clustering algorithm; Fuzzy C-Means (FCM); Knowledge transfer; Privacy protection;

D O I：

10.11999/JEIT150645

中图分类号：

学科分类号：

摘要：

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. © 2016, Science Press. All right reserved.

引用

页码：523 / 531

页数：8

共 26 条

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