教材：

王玲, 数据挖掘学习方法[D], 冶金工业出版社, 2017.7

论文：

[1] Ling Wang, Hui Zhu, Jianyao Meng. Incremental local distribution-based clustering using bayesian adaptive resonance theory[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019,30(11): 3496 - 3504.

[2] Ling Wang, Jiaoyao Meng, Ruixia Huang, Hui Zhu. Incremental feature Weighting for Fuzzy Feature Selection[J], Fuzzy Sets and Systems, 2019,357:66-76.

[3]王玲,孙华. 基于自适应学习的演化聚类算法[J]. 控制与决策, 2016,32(3):423-428

[4] Ling Wang, Jianyao Meng, Peipei Xu. Mining temporal association rules with frequent item-sets tree[J]. Applied Soft Computing, 2018,62: 817-829.

[5] Ling Wang, Qian Ma, Jianyao Meng. Incremental fuzzy association rule mining for classification and regression[J]. IEEE Access, 2019, 7:121095-121110.

[6]王玲, 孟建瑶，李俊飞. 基于多维时态关联规则的演化模糊推理预测算法[J].自动化学报, 2018,44(8):1447-1459.

[7] Ling Wang, Shulin Li, Hua Sun. A classification and regression algorithm based on quantitative association rule tree[J]. Journal of Intelligent &fuzzy system, 2016,31(3): 1407-1418.

[8] 王玲, 李树林, 徐培培, 孟建瑶.基于频繁项集树的关联规则挖掘算法[J]. 控制与决策，2018,33(4):591-599.

[9] Ling Wang, Kang Li, Qian Ma, Yanrong Lu. Hybrid dynamic learning mechanism for multivariate time series segmentation[J]. Statistical Analysis and Data Mining, 2020,13:165–177.

[10]王玲, 徐培培.基于自适应增量学习的时间序列模糊聚类算法[J].电子学报, 2019,47(5):983-991.

[11] Wang L, Zhu H, Jia GF. Adaptive G-G clustering for fuzzy segmentation of multivariate time series[J]. Stochastic Environmental Research and Risk Assessment, 2020, 34: 1353-1367.

[12] 王玲,朱慧.基于KPCA和G-G聚类的多元时间序列模糊分段[J]. 控制与决策, 2021,36(1):115-124.