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)

FERRARI D G and CASTRO L N. Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods [J]. Information Sciences, 2015, 301(1): 181-194. doi: 10.1016/j.ins.2014.12.044.

TZORTZIS G and LIKAS A. The minmax k-means clustering algorithm[J]. Pattern Recognition, 2014, 47(7): 2505-2516. doi: 10.1016/j.patcog.2014.01.015.

孙吉贵, 刘杰, 赵连宇. 聚类算法研究[J]. 软件学报, 2008, 19(1): 48-61. doi: 10.3724/SP.J.1001.2008.00048.

SUN J G, LIU J, and ZHAO L Y. Clustering algorithms research[J]. Journal of Software, 2008, 19(1): 48-61. doi: 10.3724/SP.J.1001.2008.00048.

邓赵红, 张江滨, 蒋亦樟, 等. 基于模糊子空间聚类的0阶L2型TSK模糊系统[J]. 电子与信息学报, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074.

DENG Z H, ZHANG J B, JIANG Y Z, et al. Fuzzy subspace clustering based zero-order L2-norm TSK fuzzy system[J]. Journal of Electronics Information Technology, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074.

POPAT S K and EMMANUEL M. Review and comparative study of clustering techniques[J]. International Journal of Computer Science and Information Technologies, 2014, 5(1): 805-812.

BOUGUETTAYA A, YU Q, LIU X, et al. Efficient agglomerative hierarchical clustering[J]. Expert Systems with Applications, 2015, 42(5): 2785-2797. doi: 10.1016/j.eswa. 2014.09.054.

ZHU L, CHUNG F L, and WANG S T. Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions[J]. IEEE Transactions on System, Man and Cybernetics, 2009, 39(3): 578-591. doi: 10.1109/TSMCB. 2008.2004818.

DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973, 3(3): 32-57. doi: 10.1080/ 01969727308546046.

BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York, Plenum Press, 1981: 43-93.

赵凤, 刘汉强, 范九伦. 基于互补空间信息的多目标进化聚类图像分割[J]. 电子与信息学报, 2015, 37(3): 672-678. doi: 10.11999/JEIT140371.

ZHAO F, LIU H Q, and FAN J L. Multi-objective evolutionary clustering with complementary spatial information for image segmentation[J]. Journal of Electronics Information Technology, 2015, 37(3): 672-678. doi: 10.11999/JEIT140371.

赵雪梅, 李玉, 赵泉华. 结合高斯回归模型和隐马尔可夫随机场的模糊聚类图像分割[J]. 电子与信息学报, 2014, 26(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

ZHAO X M, LI Y, and ZHAO Q H. Image segmentation by fuzzy clustering algorithm combining hidden Markov random field and Gaussian regression model[J]. Journal of Electronics Information Technology, 2014, 26(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

KIM Y H, SHIM K, KIM M S, et al. DBCURE-MR: An efficient density-based clustering algorithm for large data using MapReduce[J]. Information Systems, 2014, 42(1): 15-35. doi: 10.1016/j.is.2013.11.002.

AGRAWAL A S and BOJEWWAR S. Comparative study of various clustering techniques[J]. International Journal of Computer Science and Mobile Computing, 2014, 3(10): 497-504.

SHAO L, ZHU F, and LI X. Transfer learning for visual categorization: a survey[J]. Neural Networks and Learning, 2014, 26(5): 1019-1034. doi: 10.1109/TNNLS.2014.2330900.

LU J, BEHBOOD V, HAO P, et al. Transfer learning using computational intelligence: A survey[J]. Knowledge-based Systems, 2015, 80(1): 14-23. doi: 10.1016/j.knosys.2015. 01.010.

LONG M S, WANG J M, DING G G, et al. Transfer learning with graph co-regularization[J]. Knowledge and Data Engineering, 2014, 26(7): 1805-1818. doi: 10.1109/TKDE. 2013.97.

DAI W Y, XUE G R, YANG Q, et al. Co-clustering based classification for out-of-domain document[C]. The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, 2007: 210-219. doi: 10.1145/1281192.1281218.

GU Q and ZHOU J. Learning the shared subspace for multi- task clustering and transductive transfer classification[C]. The 2009 Ninth IEEE International Conference on Data Mining, IEEE, Washington DC, USA, 2009: 159-168. doi: 10.1109/ICDM.2009.32.

YANG Q, CHEN Y Q, XUE G R, et al. Heterogeneous transfer learning for image clustering via the social web[C]. Proceeding of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Suntec, Singapore, 2009: 1-9.

XUE G R, DAI W Y, YANG Q, et al. Topic-bridged PLSA for cross-domain text classification[C]. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, ACM, 2008: 627-634. doi: 10.1145/1390334. 1390441.

MOHAMMAD K S and SHAMS N. Analysis of KDD CUP 99 dataset using clustering based data mining[J]. International Journal of Database Theory and Application, 2013, 6(5): 23-34.

GU Q and ZHOU J. Co-clustering on manifolds[C]. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2009: 359-368. doi: 10.1145/1557019.1557063.

DAI W Y, YANG Q, XUE G R, et al. Self-taught clustering [C]. Proceeding of the 25th International Conference on Machine Learning, ACM, New York, NY, USA, 2008: 200-207. doi: 10.1145/1390156.1390182.

JING L, NG K M, and HUANG Z. An entropy weighting K-means algorithm for subspace clustering of high- dimensional sparse data[J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(8): 1026-1041. doi: 10.1109/ TKDE.2007.1048.

LIU J, MOHAMMED J, CARTER J, et al. Distance-based clustering of CGH data[J]. Bioinformatics, 2006, 22(16): 1971-1978. doi: 10.1093/bioinformatics/btl185.

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