1.山西省研究生优秀创新重点支持项目(No.20103003): 面向分类数据的聚类模型与算法研究, (主持人);

2.国家自然科学基金(No.70971080): 面向复杂数据的粗糙集多属性/多准则决策分析研究, 2010.01－2012.12, (参与);

3.高等学校博士学科点专项科研基金(No.20101401110002): 基于粒计算的符号数据分析方法研究, 2011.01－2013.12, (参与);

4.山西省青年科技基金(No.2010021016-2): 面向符号属性数据的聚类算法研究, 2010.01－2012.12, (参与)。

[1]      Liang Bai,Jiye Liang,Hangyuan Du,YikeGuo. A novel community detection algorithm based on simplification of complex networks, Knowledge-Based Systems, 2018, doi.org/10.1016/j.knosys.2017.12.007.点击阅览

[2]      Liang Bai,Jiye Liang,Yike Guo. An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters, IEEE Transactions on Fuzzy Systems, 2018, DOI 10.1109/TFUZZ.2018.2835774..点击阅览

[3]      Liang Bai,Xueqi Chen,Jiye Liang,Huawei Shen,Yike Guo. Fast density clustering strategies based on the k-means algorithm, Pattern Recognition, 2017, 71:375–386.点击阅览

[4]      Liang Bai,Xueqi Cheng,Jiye Liang,Yike Guo. Fast graph clustering with a new description model for community detection, Information Sciences, 2017, 388-389:37–47.点击阅览

[5]      杜航远,王文剑,白亮. 一种基于优化模型的演化数据流聚类方法, 中国科学(E辑:信息科学), 2017, 10.1360/N112017-00107.点击阅览

[6]      Liang Bai,Xueqi Cheng,Jiye Liang,Huawei Shen. An optimization model for clustering categorical data streams with drifting concepts, IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11):2871-2883.点击阅览

[7]      Liang Bai,Jiye Liang. Cluster validity functions for categorical data:a solution-space perspective, Data Mining and Knowledge Discovery, 2015, 29(6):1560-1597.点击阅览

[8]      Hangyuan Du,Wenjian Wang,Liang Bai. Observation noise modeling based particle filter: An efficient algorithm for target tracking in glint noise environment, Neurocomputing, 2015, 158:155–166.点击阅览

[9]      Liang Bai,Jiye Liang. The k-modes type clustering plus between-cluster information for categorical data, Neurocomputing, 2014, 133:111–121.点击阅览

[10]      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, 215:55–73.点击阅览

[11]      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.点击阅览

[12]      Liang Bai,Jiye Liang,Chao Sui,Chuangyin Dang. Fast global k-means clustering based on local geometrical information, Information Sciences, 2013, 245:168–180.点击阅览

[13]      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.点击阅览

[14]      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.点击阅览

[15]      Fuyuan Cao,Jiye Liang,Deyu Li,Liang Bai,Chuangyin Dang. A dissimilarity measure for the k-Modes clustering algorithm, Knowledge-Based Systems, 2012, 26:120-127.点击阅览

[16]      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.点击阅览

[17]      Liang Bai,Jiye Liang,Chuangyin Dang. An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data, Knowledge-Based Systems, 2011, 24(6):785-795.点击阅览

[18]      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.点击阅览

[19]      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.点击阅览

[20]      Fuyuan Cao,Jiye Liang,Liang Bai. A new initialization method for categorical data clustering, Expert Systems with Applications, 2009, 36(7):10223-10228. 点击阅览