最终学历：研究生
研究方向：数据挖掘，机器学习
电子邮箱：cfy@sxu.edu.cn
最终学位：博士
研究生导师：博士生导师
联系电话：0351-7010566
曹付元，博士，教授，博士生导师，山西大学研究生院副院长，中国计算机学会大数据专家委员会委员，中国计算机学会人工智能与模式识别专委会委员，中国人工智能学会机器学习专委会委员，山西省高等学校大数据与统计学类专业教学指导委员会委员。主要研究方向为数据挖掘、机器学习、因果推断及其在行业中的应用。近年来，先后主持国家自然科学基金面上项目2项、中国博士后基金项目（一等资助）1项、省部级项目7项、企业委托横向项目10余项；在IEEE TPAMI、IEEE TNNLS、IEEE TCYB、IEEE TKDE、IEEE TFS、AAAI、《中国科学》等国际国内重要学术刊物和会议上发表学术论文70余篇，其中SCI收录36篇；获山西省科技进步二等奖1项（排名第一），山西省科技进步一等奖1项（排名第四）、山西省教学成果奖（高等教育）特等奖、一等奖各1项（排名第二）。主讲的《机器学习》课程被认定为山西省高等学校精品共享课程。博士学位论文获2010年度山西省优秀博士学位论文奖和2011年度中国人工智能学会优秀博士学位论文奖。
[1] 面向广义多视图数据的聚类算法研究. 国家自然科学基金面上项目, 2020.01-2023.12, 主持人
[2] 面向关联关系数据的概念学习方法研究. 国家自然科学基金面上项目, 2016.01-2019.12, 主持人
[3] 面向中医电子病历大数据分析及示范应用. 山西省重点研发计划项目，2018.10-2021.12，主持人
[4] 面向跨领域数据的概念融合方法研究. 山西省留学基金项目,2016.01-2018.12, 主持人
[5] 大数据分割与融合方法研究. 山西省留学择优资助项目,2017.01-2019.12, 主持人
[6] 面向分类型块数据的聚类算法研究. 山西省自然科学基金项目, 2015.01-2017.12, 主持人
[7] 面向块数据的用户行为模式聚类算法研究. 中国博士后科学基金面上项目(一等资助), 2012.03-2013.12, 主持人
[8] 面向符号属性数据的聚类算法研究. 山西省自然科学青年基金项目, 2010.01-2012.12, 主持人
[9] 基于软计算的数据挖掘技术研究与实现. 山西高校科技研究开发项目, 2007.01-2009.12, 主持人
[10] 面向企业的智能数据分析平台. 太原市科技局项目,2007.04.-2008.03, 主持人
[1] Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang. Efficient causal structure learning from multiple interventional datasets with unknown targets. AAAI-22, Accept.
[2] Qianqing Chen, Fuyuan Cao, Ying Xing, Jiye Liang. Instance selection:a Bayesian decision theory perspective. AAAI-22, Accept.
[3] Shuai Yang, Hao Wang, Kui Yu, Fuyuan Cao, Xindong Wu. Towards efficient local causal structure learning. IEEE Transactions on Big Data, 10.1109/TBDATA.2021.3062937, 2021.
[4] Shuai Yang, Kui Yu, Fuyuan Cao, Hao Wang, Xindong Wu. Dual-Representation based autoencoder for domain adaptation. IEEE Transactions on Cybernetics, 10.1109/TCYB.2020.304076, 2021.
[5] Yang Shuai, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, Jiuyong Li. Learning causal representations for robust domain adaptation. IEEE Transactions on Knowledge and Data Engineering, Accept.
[6] Jiye Liang, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang. Incomplete multi-view clustering via local and global co-Regularization. SCIENCE CHINA Information Sciences, Accept.
[7] 杨帅, 王浩, 俞奎, 曹付元. 基于实例加权和双分类器的稳定学习算法.软件学报, Accept.
[8] Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang. An outlier detection algorithm for categorical matrix-object data. Applied Soft Computing, 2021,104, 107182.
[9] Liqin Yu, Fuyuan Cao, Xiao-Zhi Gao, Jing Liu, Jiye Liang. k-Mnv-Rep:A k-type clustering algorithm for matrix-object data. Information Sciences, 2021, 542:40-57.
[10] Liang Bai, Jiye Liang, Fuyuan Cao. Semi-supervised clustering with constraints of different types from multiple information sources. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9):3247-3258.
[11] Jing Liu, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, Jiye Liang. A cluster-weighted kernel K-means method for multi-view clustering. AAAI 2020, 4860-4867
[12] Liqin Yu, Fuyuan Cao, Xingwang Zhao, Xiaodan Yang, Jiye Liang. Combining attribute content and label information for categorical data ensemble clustering. Applied Mathematics and Computation, 2020, 381:125280.
[13] Liang Bai, Jiye Liang, Fuyuan Cao. A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters. Information Fusion, 2020, 61:36-47.
[14] Hongju Yang, Yao Li, Xuefeng Yan, Fuyuan Cao. ContourGAN:Image contour detection with generative adversarial network. Knowledge-Based Systems, 2019, 164:21-28.
[15]李顺勇, 张苗苗, 曹付元. 基于分类型矩阵对象数据的MD fuzzy k-modes聚类算法. 计算机研究与发展, 2019,56(6):1325-1337.
[16] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang, etc. An algorithm for clustering categorical data with set-valued features. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10):4593-4606.
[17] Xingwang Zhao, Fuyuan Cao, Jiye Liang. A sequential ensemble clusterings generation algorithm for mixed data. Applied Mathematics and Computation, 2018, 335:264–277.
[18] Qingqiong Cai, Fuyuan Cao, Tao Li, Hua Wang. On distances in vertex-weighted trees. Applied Mathematics and Computation, 2018, 333:435-442.
[19] Yinfeng Meng, Jiye Liang, Fuyuan Cao, Yijun He. A new distance with derivative information for functional k-means clustering algorithm. Information Sciences, 2018, 463-464:166-185.
[20]梁吉业, 乔洁, 曹付元, 刘晓琳. 面向短文本分析的分布式表示模型. 计算机研究与发展, 2018, 55(8):1631-1640.
[21] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang. A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes. Applied Mathematics and Computation, 2017, 295:1-15.
[22] Fuyuan Cao, Liqin Yu, Joshua Zhexue Huang, Jiye Liang. k-mw-modes:An algorithm for clustering categorical matrix-object data. Applied Soft Computing, 2017, 57:605-614.
[23] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang. Trend analysis of categorical data streams with a concept change method. Information Sciences, 2014, 276:160–173.
[24] Xingwang Zhao, Jiye Liang, Fuyuan Cao. A simple and effective outlier detection algorithm for categorical data. International Journal of Machine Learning and Cybernetics, 2014, 5(3):469–477.
[25] Fuyuan Cao, Jiye Liang, Deyu Li, Xingwang Zhao. A weighting K-Modes algorithm for subspace clustering of categorical data. Neurocomputing, 2013, 108:23-30.
[26] Fuyuan Cao, Joshua Zhexue Huang, A concept-drifting detection algorithm for categorical evolving data, Jian. Pei et al.(Eds.):PAKDD 2013, Part II, LNAI 7819, 492-503. Springer, Heidelberg, 2013.
[27] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. The impact of cluster representatives on the convergence of the K-Modes type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1509-1522.
[28] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A novel fuzzy clustering algorithm with between-cluster information for categorical data. Fuzzy Sets and Systems, 2013, 215(3):55-73.
[29] Ming Gao, Fuyuan Cao, Joshua Zhexue Huang. A cross cluster-based collaborative filtering method for recommendation. Proceeding of the IEEE International Conference on Information and Automation, 447-452, 2013.
[30] Fuyuan Cao, Jiye Liang, Deyu Li, Liang Bai, Chuangyin Dang. A dissimilarity measure for the K-Modes clustering algorithm. Knowledge-Based Systems, 2012, 26(1):120–127.
[31] Jiye Liang, Liang Bai, Chuangyin Dang, Fuyuan Cao. The K-Means type algorithms versus imbalanced data distributions. IEEE Transactions on Fuzzy Systems, 2012, 20(4):728-745.
[32]Jiye Liang, Xingwang Zhao, Deyu Li, Fuyuan Cao, Chuangyin Dang. Determining the number of clusters using information entropy for mixed data. Pattern Recognition, 2012, 45(6):2251-2265.
[33] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A cluster centers initialization method for clustering categorical data. Expert Systems with Applications, 2012, 39(9):8022-8029.
[34] Fuyuan Cao, Jiye Liang. A data labeling method for clustering categorical data. Expert Systems with Applications, 2011, 38(3):2381-2385.
[35] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A novel attribute weighting algorithm for clustering high-dimensional categorical data. Pattern Recognition, 2011, 44(12):2843-2861.
[36] Fuyuan Cao, Jiye Liang, Liang Bai, Xingwang Zhao, Chuangyin Dang. A framework for clustering categorical time-evolving data. IEEE Transactions on Fuzzy Systems, 2010, 18(5):872-882.
[37] 梁吉业, 白亮, 曹付元. 基于新的距离度量的K-Modes聚类算法. 计算机研究与发展, 2010, 47(10):1749-1755.
[38] Fuyuan Cao, Jiye Liang, Liang Bai. A new initialization method for categorical data clustering. Expert Systems with Applications, 2009, 36(7):10223-10228.
[39] Fuyuan Cao, Jiye Liang, Guang Jiang. An initialization method for the K-Means algorithm using neighborhood model. Computers and Mathematics with Applications, 2009, 58(3):474-483.