An improved weighted-feature clustering algorithm for k-anonymity

摘要：

Chiu proposed a clustering algorithm adjusting the numeric feature weights automatically for k-anonymity implementation and this approach gave a better clustering quality over the traditional generalization and suppression methods. In this paper, we propose an improved weightedfeature clustering algorithm which takes the weight of categorical attributes and the thesis of optimal k-partition into consideration. To show the effectiveness of our method, we do some information loss experiments to compare it with greedy k-member clustering algorithm.

关键词： k-anonymity clustering k-partition

作者: Lijian Lu Xiaojun Ye

作者单位: School of Software,Tsinghua University,Beijing,100084,P.R.China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 415-418

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）