具有隱私保護(hù)功能的知識遷移聚類算法

陳愛國; 王士同

doi:10.11999/JEIT150645

具有隱私保護(hù)功能的知識遷移聚類算法

doi: 10.11999/JEIT150645 cstr: 32379.14.JEIT150645

陳愛國¹,
王士同¹

基金項目:

國家自然科學(xué)基金(61272210)，江蘇省杰出青年基金(BK20140001)，江蘇省自然科學(xué)基金(BK20130155)

計量
- 文章訪問數(shù): 1325
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- PDF下載量: 651
- 被引次數(shù): 0
出版歷程
- 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: 傳統(tǒng)聚類算法在數(shù)據(jù)量不足或數(shù)據(jù)被污染的場景下聚類效果較差，針對此問題，在經(jīng)典模糊C均值(FCM)技術(shù)的基礎(chǔ)上，該文提出融合歷史類中心和歷史隸屬度兩類知識遷移機(jī)制的聚類算法。該算法通過有效利用歷史數(shù)據(jù)中總結(jié)得到的輔助知識來指導(dǎo)當(dāng)前由于數(shù)據(jù)不足或數(shù)據(jù)污染帶來的聚類困難問題，從而提高聚類效果。同時，由于該算法僅利用歷史數(shù)據(jù)的類中心和隸屬度，對歷史數(shù)據(jù)具有隱私保護(hù)的優(yōu)點。通過在模擬數(shù)據(jù)集和真實數(shù)據(jù)集上的仿真實驗，證明了該算法的有效性。
- 知識遷移 /
- 隱私保護(hù) /
- 聚類算法 /
- 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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