Publications

[1] Long and short-range dependency graph structure learning framework on point cloud[link]
      Jiye Liang, Zijin Du, Jianqing Liang, Kaixuan Yao, Feilong Cao.
      IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, DOI: 10.1109/TPAMI.2023.3298711.

[2] Evaluating classification model against bayes error rate [link]
      Qingqiang Chen , Fuyuan Cao , Ying Xing , Jiye Liang
      IEEE Transactions on Pattern Analysis and Machine Intelligence2023,45(8): 9639 - 9653

[3] Spectral clustering with robust self-learning constraints[link]
      Liang Bai, Minxue Qi, Jiye Liang.
      Artificial Intelligence, 2023, 320: 103924.

[4] Efficient classification by removing bayesian confusing samples [link]
    Fuyuan Cao, Qingqiang Chen, Ying Xing, Jiye Liang.
    IEEE Transactions on Knowledge and Data Engineering, [4]2023, DOI: 10.1109/TKDE.2023.3303425

[5] Random deep graph matching [link]
    Yu Xie, Zhiguo Qin, Maoguo Gong, Bin Yu, Jiye Liang
    IEEE Transactions on Knowledge & Data Engineering, [5]2023, 35(10): 10411-10422.

[6] Unsupervised dimensionality reduction based on fusing multiple clustering results [link]
    Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang.
    IEEE Transactions on Knowledge and Data Engineering, [6]2023,35(3):3211-3223.

[7] Local causal discovery in multiple manipulated datasets [link]
    Yunxia Wang, Fuyuan Cao , Kui Yu ,Jiye Liang.
    IEEE Transactions on Neural Networks and Learning Systems, [7]2023,34(10): 7235 - 7247.

[8] Adaptive prototype interaction network for few-shot knowledge graph completion [link]
    Yuling Li , Kui Yu , Member, Yuhong Zhang , Jiye Liang , Xindong Wu
    IEEE Transactions on Neural Networks and Learning Systems, [8]2023, DOI: 10.1109/TNNLS.2023.3283545

[9] K-relations-based consensus clustering with entropy-norm regularizers [link]
    Liang Bai, Jiye Liang.
    IEEE Transactions on Neural Networks and Learning Systems, [9]2023, DOI: 10.1109/TNNLS.2023.3307158

[10] RSS-bagging: improving generalization through the fisher information of training data [link]
    Yuhua Qian, Jiye Liang, Peng Song, Chuangyin Dang, Wei Wei
    IEEE Transactions on Neural Networks and Learning Systems, [10]2023, DOI: 10.1109/TNNLS.2023.3270559

[11] GUIDE: Training deep graph neural networks via guided dropout over edges [link]
    Jie Wang, Jianqing Liang, Jiye Liang, Kaixuan Yao
    IEEE Transactions on Neural Networks and Learning Systems., 2023. DOI: 10.1109/TNNLS.2022.3172879

[12] Evaluation of the decision performance of the decision rule set from an ordered decision table [link]
    Jianli Huang, Xianjie Guo, Kui Yu, Fuyuan Cao, Jiye Liang.
    IEEE Transactions on Big Data, [12]2023, DOI: 10.1109/TBDATA.2023.3285477

[13] A zero-shot learning boosting framework via concept-constrained clustering [link]
    Qin Yue, Junbiao Cui, Liang Bai , Jianqing Liang, Jiye Liang.
    Pattern Recognition, 2023,145: 109937

[14] A bi-level metric learning framework via self-paced learning weighting [link]
    Jing Yan , Wei Wei, Xinyao Guo, Chuangyin Dang, Jiye Liang
    Pattern Recognition, 2023, 139: 109446.

[15] Exploring the role of edge distribution in graph convolutional networks [link]
    Liancheng He , Liang Bai , Xian Yang, Zhuomin Liang , Jiye Liang.
    Neural Networks, 2023.

[16] Group-wise interactive region learning for zero-shot recognition [link]
    Ting Guo, Jiye Liang, Guo-Sen Xie.
    Information Sciences, 2023, 642: 119135.

[17] High-order graph attention network [link]
    Liancheng He, Liang Bai, Xian Yang, Hangyuan Du, Jiye Liang.
     Information Sciences, 2023, 630: 222-234.

[18] Multi-actor mechanism for actor-critic reinforcement learning [link]
    Lin Li, Yuze Li, Wei Wei , Yujia Zhang, Jiye Liang.
    Information Sciences, 2023, 647: 119494.

[19] Multiple metric learning via local metric fusion [link]
    Xinyao Guo, Lin Li, Chuangyin Dang, Jiye Liang, Wei Wei.
    Information Sciences, [19]2023, 621: 341-353.

[20] A new contrastive learning framework for reducing the effect of hard negatives [link]
    Cui Wentao, Liang Bai, Xian Yang, Jiye Liang.
    Knowledge-Based Systems, 2023, 260: 110121.

[21] graph clustering network with weighting mechanism and collaborative training [link]
    Jing Liu, Fuyuan Cao, Xuechun Jing, Jiye Liang.
    Expert Systems with Applications, 2023, 236: 121298.

[22] Evaluation of the decision performance of the decision rule set from an ordered decision table [link]
    Baoli Wang, Jiye Liang, Yiyu Yao.
    Artifcial Intelligence Review, 2023,56(1): 533-575

[1]A General Representation Learning Framework with Generalization Performance Guarantees[link]
      Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang
      Proceedings of the 40th International Conference on Machine Learning, PMLR, 2023, 202: 6522-6544

[2]Graph Neural Networks with Interlayer Feature Representation for Image Super-Resolution [link]
      Shenggui Tang, Kaixuan Yao, Jianqing Liang, Zhiqiang Wang, Jiye Liang
      WSDM2023, 652-660

[1]Graph convolutional autoencoders with co-learning of graph structure and node attributes [link]
      Jie Wang, Jiye Liang, Kaixuan Yao, Jianqing Liang, Dianhui Wang
      Pattern Recognition, 2022, 121,108215.

[2] Local-global coupling relationship based low-light image enhancement [link]
      Keqi Wang, Yuhua Qian, Jiye Liang, Chang Liu, Qin Huang, Lu Chen, Jieru jia
      SCIENTIA SINICA Informationis,2022, 52(3): 443-460.

[3] A group incremental approach for feature selection on hybrid data [link]
      Feng Wang, Wei Wei, Jiye Liang
      Soft Computing,2022, 26:3663–3677.

[4] A bayesian matrix factorization model for dynamic user embedding in recommender system [link]
      Kaihan Zhang, Zhiqiang Wang, Jiye Liang, Xingwang Zhao
      Frontiers of Computer Science,2022, 16(5): 165346.

[5] Incomplete multi-view clustering via local and global co-regularization [link]
      Jiye Liang, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang
      SCIENCE CHINA Information Sciences,2022, Doi: 10.1007/s11432-020-3369-8.

[6] Incomplete multi-view clustering algorithm based on multi-order neighborhood diffusion and fusion [link]
      Xiaolin Liu, Liang Bai, Xingwang Zhao, Jiye Liang
      Journal of Software,2022, 33(4):1354−1372.

[7] Weak multi-label learning with missing labels via instance granular discrimination [link]
      Anhui Tan, Xiaowan Ji, Jiye Liang, Yuzhi Tao, Wei-Zhi Wu, Witold Pedrycz
      Information Sciences,2022, 594:200-216.

[8] AF: An Association-based Fusion Method for Multi-Modal Classification [link]
      Xinyan Liang, Yuhua Qian, Qian Guo, Honghong Cheng, Jiye Liang
      IEEE Transactions on Pattern Analysis and Machine Intelligence,2022, doi: 10.1109/TPAMI.2021.3125995.

[9] Multi-view graph convolutional networks with attention mechanism [link]
      Kaixuan Yao, Jiye Liang, Jianqing Liang, Ming Li, Feilong Cao
      Artificial Intelligence,2022, 307: 103708.

[10] A trilevel analysis of uncertainty measuresin partition-based granular computing [link]
      Baoli Wang, Jiye Liang, Yiyu Yao
      Artifcial Intelligence Review,2022, https://doi.org/10.1007/s10462-022-10177-6.

[11] A categorical data clustering framework on graph representation [link]
      Liang Bai, Jiye Liang
      Pattern Recognition,2022, 128:108694.

[12] Cross-modal propagation network for generalized zero-shot learning [link]
      Ting Guo, Jianqing Liang, Jiye Liang, Guo-Sen Xie
      Pattern Recognition Letters,2022, 159:125-131.

[13] GUIDE: Training deep graph neural networks via guided dropout over edges [link]
      Jie Wang, Jiye Liang, Jiye Liang, Kaixuan Yao, Jiye Liang, Guo-Sen Xie
      IEEE Transactions on Neural Networks and Learning Systems,2022. DOI: 10.1109/TNNLS.2022.3172879.

[14] Self-constrained spectral clustering [link]
      Liang Bai,Jiye Liang,Yunxiao Zhao
      IEEE Transactions on Pattern Analysis and Machine Intelligence,2022, Doi:10.1109/TPAMI.2022.3188160.

[15] Metric Learning via Perturbing Hard-to-classify Instances [link]
      Xinyao Guo, Wei Wei, Jianqing Liang, Chuangyin Dang, Jiye Liang
      Pattern Recognition,2022, 132:108928.

[16] Semi-supervised partial multi-label classification via consistency learning [link]
      Anhui Tan, Jiye Liang, Weizhi Wu, Jia Zhang
      Pattern Recognition,2022, 131:108839.

[17] Self-supervised spectral clustering with exemplar constraints [link]
      Liang Bai, Yunxiao Zhao,Jiye Liang
      Pattern Recognition,2022, 132: 108975.

[18] Centroids-guided deep multi-view K-means clustering [link]
      Jing Liu, Fuyuan Cao, Jiye Liang,Jiye Liang
      Information Sciences,2022: 876-896.

[1]Instance selection: A bayesian decision theory perspective [link]
     Qingqiang Chen, Fuyuan Cao, Ying Xing, Jiye Liang
      In Proc. of the 36th AAAI Conf. on Artificial Intelligence, 2022, Feb. 22-Mar. 1.

[2]Efficient causal structure learning from multiple interventional datasets with unknown targets [link]
      Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang
      In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI'22), Feb. 22-Mar. 1, 2022.

[3]Controlling underestimation bias in reinforcement learning via quasi-median operation [link]
      Wei Wei, Yujia Zhang, Jiye Liang, Lin Li, Yuze Li
      In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI'22), 2022，Feb. 22-Mar. 1.

[4]Multi-scale variational graph autoencoder for link prediction [link]
      Zhihao Guo, Feng Wang, Kaixuan Yao, Jiye Liang, Zhiqiang Wang
      In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining(WSDM '22), 334–342, 2022.

[5]Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification [link]
      Qin Yue, Jiye Liang, Junbiao Cui, Liang Bai
      KDD 2022，2408-2417,2022.

[1]k-Mnv-Rep: a k-type clustering algorithm for matrix-object data [link]
      Liqin Yu,Fuyuan Cao, Xiao-Zhi Gao,Jing Liu,Jiye Liang
      Information Sciences, 2021, 542:40-57.

[2]A community detection algorithm based on graph compression for large-scale social networks [link]
      Xingwang Zhao, Jiye Liang, Jie Wang
       Information Sciences,2021, 551：358-372.

[3] Liqin Yu, Jiye Liang. An outlier detection algorithm for categorical matrix-object data [link]
       Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang
       Applied Soft Computing, 2021, 104:107182.

[4] A unified sample selection framework for output noise filtering: An error-bound perspective [link]
       Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang
       Journal of Machine Learning Research,2021,22(18):1−66.

[5] Accelerating ReliefF using information granulation [link]
       Wei Wei, Da Wang, Jiye Liang
       International Journal of Machine Learning and Cybernetics, 2021, DOI: 10.1007/s13042-021-01334-4.

[6] Graph-based semi-supervised learning via improving the quality of the graph dynamically [link]
       Jiye Liang, Junbiao Cui, Jie Wang, Wei Wei
       Machine Learning, 2021, 110:1345–1388.

[7] Deep neural network compression through interpretability-based filter pruning [link]
       Kaixuan Yao, Feilong Cao, Yee Leung, Jiye Liang
       Pattern Recognition, 2021, 119:108056.

[8] Semi-supervised learning with mixed-order graph convolutional networks [link]
       JieWang, Jianqing Liang, Junbiao Cui, Jiye Liang
       Information Sciences, 2021, 573: 171-181.

[9] Semi-supervised clustering with constraints of different types from multiple information sources [link]
       Liang Bai,Jiye Liang,Fuyuan Cao
       IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9):3247-3258.

[10] Unsupervised dimensionality reduction based on fusing multiple clustering results [link]
       Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang
       IEEE Transactions on Knowledge and Data Engineering, 2021, 10.1109

[11] A method on long tail recommendation based on three-factor probabilistic graphical model [link]
       Chenjiao Feng, Peng Song, Zhiqiang Wang, Jiye Liang
       Journal of Computer Research and Development,, 2021, 58(9):1975-1986.(in Chinese)

[12] Logic could be learned from images, International Journal of Machine Learning and Cybernetics [link]
       Qian Guo, Yuhua Qian,Xinyan Liang,Yanhong She, Deyu Li, Jiye Liang
       International Journal of Machine Learning and Cybernetics, 2021, 12:3397–3414.

[13] Metric learning with clustering-based constraints [link]
       Xinyao Guo, Chuangyin Dang, Jianqing Liang, Wei Wei, Jiye Liang
       International Journal of Machine Learning and Cybernetics, 2021,12:3597-3605.

[14]Fuzzy rough discrimination and label weighting for multi-label feature selection [link]
      Anhui Tan, Jiye Liang, Wei-Zhi Wu, Jia Zhang, Lin Sun, Chao Chen, J. Gao
      Neurocomputing, 2021, 465:128-140.

[1] Deviation degree: A perspective on score functions in hesitant fuzzy sets [link]
      Baoli Wang, Jiye Liang, Jifang Pang
      International Journal of Fuzzy Systems, vol. 21(7), 2299-2317, 2019.

[2] Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes [link]
      Chao Zhang, Deyu Li,Jiye Liang
      Information Sciences, vol. 507, 665-683, 2020.

[3] Interval-valued hesitant fuzzy multi-granularity three-way decisions in consensus processes with applications to multi-attribute group decision making [link]
      Chao Zhang, Deyu Li,Jiye Liang
      Information Sciences, vol. 511, 192-211, 2020.

[4] Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions [link]
      Jifang Pang, Xiaoqiang Guan,Jiye Liang,Baoli Wang, Peng Song
      International Journal of Approximate Reasoning, vol. 32(19), e5778, 2020.

[5] A naive learning algorithm for class-bridge-decomposable multidimensional Bayesian network classifiers [link]
      Yali Lv,Weixin Hu, Jiye Liang, Yuhua Qian, Junzhong Miao
      Concurrency and Computation: Practice and Experience, vol. 32(19), e5778, 2020.

[6] A fusion collaborative filtering method for sparse data in recommender systems [link]
      Chenjiao Feng, Jiye Liang, Peng Song, Zhiqiang Wang
      Information Sciences, vol. 521, 365-379, 2020.

[7] Combining attribute content and label information for categorical data ensemble clustering [link]
      Liqin Yu, Fuyuan Cao, Xingwang Zhao, Xiaodan Yang,Jiye Liang
      Applied Mathematics and Computation, vol. 381, 125280, 2020.

[8] Clustering method based on sample's stability,Scientia Sinica Informationis [link]
      Feijiang Li, Yuhua Qian, Jiye Liang, Jiye Liang, Wenjian Wang
      Scientia Sinica Informationis(In Chinese), vol. 50(8), 1239-1254, 2020.

[9] Association mining method based on neighborhood [link]
      Honghong Cheng, Yuhua Qian, Zhiguo Hu, Jiye Liang
      Scientia Sinica Informationis(In Chinese), vol. 50(6), 824-844, 2020.

[10] An accelerator for the logistic regression algorithm based on sampling on-demand [link]
      Jiye Liang, Yunsheng Song, DeYu Li, Zhiqiang Wang, Chuangyin Dang
      SCIENCE CHINA Information Sciences, vol. 63(6), 169102, 2020.

[11] A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters [link]
      Liang Bai,Jiye Liang,Fuyuan Cao
      Information Fusion, vol. 61, 36-47, 2020.

[12] New label propagation algorithm with pairwise constraints [link]
      Liang Bai, Junbin Wang, Jiye Liang, Hangyuan Du
      Pattern Recognition, vol. 106, Article107411, 2020.

[13] Linear regularized functional logistic model [link]
      Yinfeng Meng,Jiye Liang
      Pattern Recognition, vol. 106, 1617-1626, 2020.

[14] Deconvolutional neural network for image super-resolution [link]
      Feilong Cao, Kaixuan Yao, Jiye Liang
      Neural Networks, vol. 132, 394–404, 2020.

[1] A cluster-weighted kernel K-Means method for multi-view clustering [link]
      Jing Liu,Fuyuan Cao,Xiao-Zhi Gao,Liqin Yu,Jiye Liang
      In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI'20), New York, NY, USA, Feb. 7-Feb. 12, 2020

[2] A three-level optimization model for nonlinearly separable clustering [link]
      Liang Bai, Jiye Liang
      In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI'20), New York, NY, USA, Feb. 7-Feb. 12, 2020

[3] Sparse subspace clustering with entropy-norma [link]
      Liang Bai,Jiye Liang
      Proceedings of the 37th International Conference on Machine Learning(ICML2020), Vienna, Austria,2020-07-12 - 2020-07-17

[1] A stratified sampling based clustering algorithm for large-scale data [link]
      Xingwang Zhao, Jiye Liang, Chuangyin Dang
      Knowledge-Based Systems, vol. 163, 416-428, 2019.

[2] Information fusion in rough set theory : An overview [link]
      Wei Wei,Jiye Liang
      Information Fusion, vol. 48, 107-118, 2019.

[3] Intuitionistic fuzzy rough set-based granular structures and attribute subset selection [link]
      Zhiqiang Wang , Jiye Liang, Ru Li
      Journal of Computer Research and Development(In Chinese), vol. 56(2), 306-318, 2019.

[4] Intuitionistic fuzzy rough set-based granular structures and attribute subset selection [link]
      Anhui Tan, Weizhi Wu, Yuhua Qian,Jiye Liang, Jinkun Chen, Jinjin Li
      IEEE Transactions on Fuzzy Systems, vol. 27(3), 527-539, 2019.

[5] Hierarchical division clustering framework for categorical data [link]
      Wei Wei,Jiye Liang, Xinyao Guo, Peng Song,Yijun Sun
      Neurocomputing, vol. 341, 118-134, 2019.

[6] A novel edge rewiring strategy for tuning structural properties in networks [link]
      Junfang Mu, Wenping Zheng, Jiye Liang, Jiye Liang
      Knowledge-Based Systems, vol. 177, 55-67, 2019.

[7] Protein complex detection algorithm based on multiple topological characteristics in PPI networks [link]
      Jie Wang, Jiye Liang, Wenping Zheng, Xingwang Zhao, Junfang Mu
      Information Sciences, vol. 489, 78-92, 2019.

[8] An ensemble classification algorithm based on information entropy for data streams [link]
      Junhong Wang, Shuliang Xu, Bingqian Duan, Caifeng Liu,Jiye Liang
      Neural Processing Letters, vol. 50, 2101-2117, 2019.

[9] An accelerator for support vector machines based on the local geometrical information and data partition [link]
      Yunsheng Song,Jiye Liang,Feng Wang
      International Journal of Machine Learning and Cybernetics, vol. 10, 2389-C2400, 2019.

[10] Accelerating incremental attribute reduction algorithm by compacting a decision table [link]
      Wei Wei,Peng Song,Jiye Liang, Xiaoying Wu
      International Journal of Machine Learning and Cybernetics, vol. 10(9), 2355-2373, 2019.

[11] An information-theoretical framework for cluster ensemble [link]
      Liang Bai, Jiye Liang, Hangyuan Du, Yike Guo
      IEEE Transactions on Knowledge and Data Engineering, vol. 31(8), 1464-1477, 2019.

[1] A collaborative filtering recommendation algorithm based on information of community experts
      Zhang Kaihan, Liang Jiye, Zhao Xingwang, Wang Zhiqiang
      Journal of Computer Research and Development, 2018, 55: 968-976.

[2] Discernibility matrix based incremental attribute reduction for dynamic data [link]
      Wei Wei, Xiaoying Wu, Jiye Liang, Junbiao Cui, Yijun Sun
      Knowledge-Based Systems, 2018, 140: 142-157.

[3] A novel community detection algorithm based on simplification of complex networks [link]
      Liang Bai, Jiye Liang, Hangyuan Du, YikeGuo
      Knowledge-Based Systems, 2018, 143: 58-64.

[4] A cautious ranking methodology with its application for stock screening [link]
      Peng Song, Jiye Liang, Yuhua Qian, Wei Wei, Feng Wang
      Applied Soft Computing, 2018, 71: 835-848.

[5] Multi-view data ensemble clustering: A cluster-level perspective [link]
      Jiye Liang, Qianyu Shi, Xingwang Zhao
      International Journal of Machine Intelligence and Sensory Signal Processing, 2018, 2: 97-120.

[6] Hesitant fuzzy linguistic rough set over two universes model and its applications [link]
      Chao Zhang, Deyu Li, Jiye Liang
      International Journal of Machine Learning and Cybernetics, 2018, 9: 577-588.

[7] Local rough set: a solution to rough data analysis in big data [link]
      Yuhua Qian, Xinyan Liang, Qi Wang, Jiye Liang, Bing Liu, Andrzej Skowron, et al
      International Journal of Approximate Reasoning, 2018, 97: 38-63.

[8] Review on hierarchical learning methods for large-scale classification task [link]
      Qinghua Hu, Yu Wang, Yucan Zhou, Hong Zhao, Yuhua Qian, Jiye Liang
      Scientia Sinica Informationis, 2018, 48: 487-500.

[9] Exploiting user-to-user topic inclusion degree for link prediction in social-information networks [link]
      Zhiqiang Wang, Jiye Liang, Ru Li
      Expert Systems with Applications, 2018, 108: 143-158.

[10] A sequential ensemble clusterings generation algorithm for mixed data [link]
      Xingwang Zhao, Fuyuan Cao, Jiye Liang
      Applied Mathematics and Computation, 2018, 335: 264-277.

[11] A fusion probability matrix factorization framework for link prediction [link]
      Zhiqiang Wang, Jiye Liang, Ru Li
      Knowledge-Based Systems, 2018, 159: 72-85.

[12] A new distance with derivative information for functional k-means clustering algorithm [link]
      Yinfeng Meng, Jiye Liang, Fuyuan Cao, Yijun He
      Information Sciences, 2018, 463-464: 166-185.

[13] A distributed representation model for short text analysis
      Jiye Liang, Jie Qiao, Fuyuan Cao, Xiaolin Liu
      Journal of Computer Research and Development, 2018, 55: 1631-1640.

[14] An Algorithm for Clustering Categorical Data with Set-valued Features [link]
      Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang, Xingwang Zhao, Yinfeng Meng
      IEEE Transactions on Neural Networks and Learning Systems, 2018, 29: 4593-4606.

[15] An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters [link]
      Liang Bai, Jiye Liang, Yike Guo
      IEEE Transactions on fuzzy systems, 2018, 26: 3524-3533.

[1] A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes [link]
      Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang
      Applied Mathematics and Computation, 2017, 295: 1-15.

[2] An efficient instance selection algorithm for k nearest neighbor regression [link]
      Yunsheng Song, Jiye Liang, Jing Lu, Xingwang Zhao
      Neurocomputing, 2017, 251: 26-34.

[3] Local multigranulation decision-theoretic rough sets [link]
      Yuhua Qian, Xinyan Liang, Guoping Lin, Qian Guo, Jiye Liang
      International Journal of Approximate Reasoning, 2017, 82: 119-137.

[4] Grouping granular structures in human granulation intelligence [link]
      Yuhua Qian, Honghong Cheng, Jieting Wang, Jiye Liang, Witold Pedrycz, Chuangyin Dang
      Information Sciences, 2017, 382-383: 150-169.

[5] Multigranulation information fusion: A Dempster-Shafer evidence theory-based clustering ensemble method [link]
      Feijiang Li, Yuhua Qian, Jieting Wang, Jiye Liang
      Information Sciences, 2017, 378: 389-409.

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

[7] k-mw-modes: An algorithm for clustering categorical matrix-object data [link]
      Fuyuan Cao, Liqin Yu, Joshua Zhexue Huang, Jiye Liang
      Applied Soft Computing, 2017, 57: 605-614.

[8] An adaptive consensus method for multi-attribute group decision making under uncertain linguistic environment [link]
      Jifang Pang, Jiye Liang, Peng Song
      Applied Soft Computing, 2017, 58: 339-353.

[9] Clustering ensemble selection for categorical data based on internal validity indices [link]
      Xingwang Zhao, Jiye Liang, Chuangyin Dang
      Pattern Recognition, 2017, 69: 150-168.

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

[11] A multi-view OVA model based on decision tree for multi-classification tasks [link]
      Xiaoqiang Guan, Jiye Liang, Yuhua Qian, Jifang Pang
      Knowledge-Based Systems, 2017, 138: 208-219.

[12] A seed expansion graph clustering method for protein complexes detection in protein interaction networks [link]
      Jie Wang, Wenping Zheng, Yuhua Qian, Jiye Liang
      Molecules, 2017, 22: 2179.

[1] Decision-theoreticroughsetsunderdynamicgranulation [link]
      Yanli Sang, Jiye Liang, Yuhua Qian
      Knowledge-Based Systems, 2016, 91:84-92.

[2] A fuzzy multigranulation decision-theoretic approach to multi-source fuzzy information systems [link]
      GuopingLin, Jiye Liang, Yuhua Qian, JinjinLi
      Knowledge-Based Systems, 2016, 91:102-113.

[3] Comparison study of orthonormal representations of functional data in classification [link]
      Yinfeng Meng, Jiye Liang, Yuhua Qian
      Knowledge-Based Systems, 2016, 97:224–236.

[4] Research on question answering for reading comprehension based on Chinese discourse frame semantic parsing [link]
      Zhiqiang Wang, Ru Li, Jiye Liang, Xuhua Zhang, Juan Wu, Na Su
      Chinese Journal of Computers, 2016, 39(4):795-807.

[5] A survey on correlation analysis of big data [link]
      Jiye Liang, Chenjiao Feng, Peng Song
      Chinese Journal of Computers, 2016, 39(1):1-18.

[6] An attribute weighted clustering algorithm for mixed data based on information entropy [link]
      Xingwang Zhao, Jiye Liang
      Journal of Computer Research and Development, 2016, 53(5): 1018-1028.

[7] An efficient feature selection algorithm for hybrid data [link]
      Feng Wang, Jiye Liang
      Neurocomputing, 2016, 193:33–41.

[8] Fuzzy rough approximations for set-valued data [link]
      Wei Wei, Junbiao Cui, Jiye Liang, Junhong Wang
      Information Sciences, 2016, 360:181–201.

[9] Space structure and clustering of categorical data [link]
      Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, Chuangyin Dang
      IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(10):2047-2059.

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

[11] An approach to cold-start link prediction:establishing connections between non-topological and topological information [link]
      Zhiqiang Wang, Jiye Liang, Ru Li, Yuhua Qian
      IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11):2857- 2870.

[1] Fuzzy-rough feature selection accelerator [link]
      Yuhua Qian, Qi Wang, Honghong Cheng, Jiye Liang, Chuangyin Dang
      Fuzzy Sets and Systems, 2015, 258: 61–78.

[2] Uncertainty measures for multigranulation approximation space [link]
      Guoping Lin, Jiye Liang, Yuhua Qian
      International Journal of Uncertianty, Fuzziness and Knowledge-Based Systems, 2015, 23(3):443–457.

[3] An improved incremental nonlinear dimensionality reduction for isometric data embedding [link]
      Xiaofang Gao, Jiye Liang
      Information Processing Letters, 2015, 115(4):492–501.

[4] A normalized numerical scaling method for the unbalanced multi-granular linguistic sets [link]
      Baoli Wang, Jiye Liang, Yuhua Qian, Chuangyin Dang
      Artificial Intelligence, vol. 307, 103708, 2022.

[5] Fusing monotonic decision trees [link]
      Yuhua Qian, Hang Xu, Jiye Liang, Bing Liu, Jieting Wang
      IEEE Transactions on Knowledge and Data Engineering, 2015, 27(10):2717-2728.

[6] An information fusion approach by combining multigranulation rough sets and evidence theory [link]
      Guoping Lin, Jiye Liang, Yuhua Qian
      Information Sciences, 2015, 314:184–199.

[7] Compacted decision tables based attribute reduction [link]
      Wei Wei, Junhong Wang, Jiye Liang, Xin Mi, Chuangyin Dang
      Knowledge-Based Systems, 2015, 86:261-277.

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

[9] Theory and method of granular computing for big data mining [link]
      Jiye Liang, Yuhua Qian, Deyu Li, Qinghua Hu
      Science in China-Series F: Information Sciences, 2015, 45(11):1355-1369.

[10] Fuzzy granular structure distance [link]
      Yuhua Qian, Jiye Liang, Chuangyin Dang
      IEEE Transactions on Fuzzy Systems, 2015, 23(6):2245-2259.

[1] Decision-Oriented Rough Set Methods [link]
      Jiye Liang
      15th International Conference, RSFDGrC, 2015.
2014-Journals

[1] Trend analysis of categorical data streams with a concept change method [link]
      Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang
      Information Sciences, 2014, 276: 160-173.

[2] Set-Based Granular Computing: a Lattice Model [link]
      Yuhua Qian, Hu Zhang, Feijiang Li, Qinghua Hu, Jiye Liang
      International Journal of Approximate Reasoning, 2014, 55(3): 834–852.

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

[4] Multigranulation decision-theoretic rough sets [link]
      Yuhua Qian, Hu Zhang, Yanli Sang, Jiye Liang
      International Journal of Approximate Reasoning, 2014, 55: 225-237.

[5] Pessimistic rough set based decisions: A multigranulation fusion strategy [link]
      Yuhua Qian, Shunyong Li, Jiye Liang, Zhongzhi Shi, Feng Wang
      Information Sciences, 2014, 264: 196–210.

[6] Preorder information based attributes weights learning in multi-attribute decision making [link]
      Baoli Wang, Jiye Liang, Yuhua Qian
      Fundamenta Informaticae, 2014, 132: 331-347.

[7] A group incremental approach to feature selection applying rough set technique [link]
      Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua Qian
      IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 294-308.

[1] Attribute reduction for dynamic data sets [link]
      Feng Wang, Jiye Liang, Chuangyin Dang
      Applied Soft Computing, 2013, 13(1): 676-689.

[2] Attribute reduction: A dimension incremental strategy [link]
      Feng Wang, Jiye Liang, Yuhua Qian
      Knowledge-Based Systems, 2013, 39: 95-108.

[3] A novel fuzzy clustering algorithm with between-cluster information for categorical data [link]
      Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao
      Fuzzy Sets and Systems, 2013, 215: 55–73.

[4] An accelerator for attribute reduction based on perspective of objects and attributes [link]
      Jiye Liang, Junrong Mi , Wei Wei, Feng Wang
      Knowledge-Based Systems, 2013, 44: 90–100.

[5] A weighting k-Modes algorithm for subspace clustering of categorical data [link]
      Fuyuan Cao, Jiye Liang, Deyu Li, Xingwang Zhao
      Neurocomputing, 2013, 108: 23-30.

[6] Can fuzzy entropies be effective measures for evaluating the roughness of a rough set [link]
      Wei Wei, Jiye Liang, Yuhua Qian, Chuangyin Dang
      Information Sciences, 2013, 232: 143-166.

[7] Decision-relative discernibility matrixes in the sense of entropies [link]
      Wei Wei, Jiye Liang, Junhong Wang, Yuhua Qian
      International Journal of General Systems, 2013, 42(7): 721-738.

[8] Multigranulation rough sets: from partition to covering [link]
      Guoping Lin, Jiye Liang, Yuhua Qian
      Information Sciences, 2013, 241: 101-118.

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

[10] Manifold learning algorithm DC-ISOMAP of data lying on the well-separated multi-manifold with same intrinsic dimension
      Xiaofang Gao, Jiye Liang
      Journal of Computer Research and Development, 2013, 50(8): 1690-1699.

[11] The impact of cluster representatives on the convergence of the K-Modes type clustering [link]
      Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao
      IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1509-1522.

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

[2] A cluster centers initialization method for clustering categorical data [link]
    Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao.
    Expert Systems with Applications,2012, 26: 120–127.

[3] A two-grade approach to ranking interval data [link]
    Peng Song, Jiye Liang, Yuhua Qian
    Knowledge-Based Systems , 2012,27: 234-244

[4]A comparative study of rough sets for hybrid data [link]
    Wei Wei, Jiye Liang, Yuhua Qian
    Information Sciences, 2012, 190: 1-16.

[5]Partial orderings of information granulations: a further investigation
    Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang.
    Expert Systems, 2012, 29(1): 3-24.

[6]Evaluation of the results of multi-attribute group decision-making with linguistic information [link]
    Jifang Pang, Jiye Liang
    Omega, 2012, 40: 294-301.

[7]Determining the number of clusters using information entropy for mixed data [link]
    Jiye Liang, Xingwang Zhao, Deyu Li, Fuyuan Cao, Chuangyin Dang
    Pattern Recognition, 2012, 45: 2251–2265.

[8]Distance:  a more comprehensible perspective for measures in rough set theory [link]
    Jiye Liang, Ru Li, Yuhua Qian
    Knowledge-Based Systems, 2012, 27: 126-136.

[9] An efficient rough feature selection algorithm with a multi-granulation view [link]
    Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua Qian
    International Journal of Approximate Reasoning, 2012, 53: 912-926.

[10] The k-means-type algorithms versus imbalanced data distributions [link]
    Jiye Liang, Liang Bai, Chuangyin Dang, Fuyuan Cao
    IEEE Transactions on Fuzzy Systems, 2012, 20(4): 728-745.

[12] Evaluation of the decision performance of the decision rule set from an ordered decision table [link]
    Yuhua Qian, Jiye Liang, Peng Song, Chuangyin Dang, Wei Wei
    Knowledge-Based Systems, 2012, 36: 39–50.

[1]Information granularity and granular structure in decision making [link]
    Baoli Wang,Jiye Liang, Yuhua Qian
    RSKT, 2012: 440-449.

[2]Variable precision multi-granulation rough set [link]
    Wei Wei, Jiye Liang, Yuhua Qian, Feng Wang
    IEEE International Conference on Granular Computing, 2012: 639-643.

[3]Feature selection for large-scale data sets in GrC [link]
    Jiye Liang
    IEEE International Conference on Granular Computing, 2012: 2-7.

[1]The dynamical neighborhood selection based on the sampling density and manifold curvature for isometric data embedding [link]
    Xiaofang Gao, Jiye Liang
    Pattern Recognition Letters, 2011, 32(2): 202-209.

[2] A data labeling method for clustering categorical data [link]
    Xiaofang Gao, Jiye Liang
    Expert Systems with Applications,2011,38(3): 2381-2385.

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

[4]A novel attribute weighting algorithm for clustering high-dimensional categorical data [link]
       Liang Bai,Jiye Liang, Chuangyin Dang, Fuyuan Cao
    Pattern Recognition, 2011,44(12): 2843-2861.

[5]A positive approximation based accelerated algorithm to feature selection from incomplete decision tables [link]
    Yuhua Qian, Jiye Liang, Feng Wang
    Journal of Computer, 2011, 34(3): 435-442.

[6]Information granularity in fuzzy binary GrC model [link]
    Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang
    IEEE Transactions on Fuzzy Systems, 2011, 19(2): 253 – 264.

[7]An efficient accelerator for attribute reduction from incomplete data in rough set framework [link]
    Yuhua Qian, Jiye Liang, Witold Pedrycz, Chuangyin Dang
    Pattern Recognition , 2011, 44(8): 1658–1670.

[1]Closed-label concept lattice based rule extraction approach [link]
    Junhong Wang, Jiye Liang, Yuhua Qian
    ICIC, 2011: 690-698.

[2]An efficient fuzzy-rough attribute reduction approach [link]
    Yuhua Qian, Chao Li, Jiye Liang
    RSKT, 2011: 63-70.

[3]How to organize data with measurement errors? [link]
    Yuhua Qian, Jiye Liang
    SMC, 2011: 3096-3101.

[4]Uncertainty and feature selection in rough set theory [link]
    Jiye Liang
    RSKT, 2011: 8-15.

[1] On dominance relations in disjunctive set-valued ordered information systems [link]
      Yuhua Qian, Jiye Liang, Peng Song, Chuangyin Dang
      International Journal of Information Technology & Decision Making, 2010, 9(1): 9-33

[2] Incomplete multigranulation rough set [link]
      Yuhua Qian, Jiye Liang, Chuangyin Dang
      IEEE Trasactions on Systems, Man and Cybernetics-Part A, 2010, 40(2):420-431

[3] Approximation reduction in inconsistent incomplete decision tables [link]
      Yuhua Qian, Jiye Liang, Deyu Li, Feng Wang, Nannan Ma
      Knowledge-Based Systems, 2010, 23(5) : 427-433

[4] Comparative study of decision performance of decision tables induced by attribute reductions [link]
      Wei Wei, Jiye Liang, Yuhua Qian, Feng Wang, Chuangyin Dang
      International Journal of General Systems, 2010, 39(8): 813-838

[5] K-modes clustering algorithm based on a new distance measure [link]
      Jiye Liang, Liang Bai, Fuyuan Cao
      Journal of Computer Research and Development, 2010, 47(10): 1749-1755

[6] A framework for clustering categorical time-evolving data [link]
      Fuyuan Cao, Jiye Liang, Liang Bai, Xingwang Zhao, Chuangyin Dang
      IEEE Transactions on Fuzzy Systems, 2010, 18(5)：872-882.

[7] MGRS: a mulit-granulation rough set [link]
      Yuhua Qian, Jiye Liang, Yiyu Yao, Chuangyin Dang
      Information Sciences, 2010, 180: 949-970.

[8] Positive approximation: an accelerator for attribute reduction in rough set theory [link]
      Yuhua Qian, Jiye Liang, Witold Pedrycz, Chuangyin Dang
      Artificial Intelligence, 2010, 174: 597-618.

[1] On partial order relations in granular computing [link]
     Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei
     GrC, 2010, pp. 102-106.

[2] A heuristic method to attribute reduction for concept lattice [link]
     Junhong Wang, Jiye Liang, Yuhua Qian
     ICMLC, 2010, pp. 483-487.

[1] Gini-index genetic algorithm for the scheduling problems with similar characteristics [link]
      Xiaomei Yang, Jiye Liang, Jianchao Zeng, Jiahua Liang
      Journal of Systems Engineering, 2009, 24(3): 322-328.

[2] A new method for measuring the uncertainty in incomplete information systems [link]
      Yuhua Qian, Jiye Liang, Feng Wang
      International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009, 17(6): 855-880.

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

[4] A new measure of uncertainty based on knowledge granulation for rough sets [link]
      Jiye Liang, Junhong Wang, Yuhua Qian
      Information Sciences, 2009, 17(9): 458-470.

[5] Knowledge structure, knowledge granulation and knowledge distance in a knowledge base [link]
      Yuhua Qian, Jiye Liang, Chuangyin Dang
      International Journal of Approximate Reasoning, 2009, 50: 174-188.

[6] Set-valued ordered information systems [link]
      Yuhua Qian, Chuangyin Dang, Jiye Liang, Dawei Tang
      Information Sciences, 2009, 179 : 2809-2832.

[1] Apply inversion order number genetic algorithm to the job shop scheduling problem [link]
     Xiaomei Yang, Jianchao Zeng, Jiye Liang
     WGEC, 2009, pp. 196-200.

[2] A time-reduction strategy to feature selection in rough set theory [link]
     Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei, Feng Wang
     RSKT, 2009, pp. 111-119.

[3] An attribute reduction approach and its accelerated version for hybrid data [link]
     Wei Wei, Jiye Liang, Yuhua Qian, Feng Wang
     IEEE ICCI, 2009, pp. 167-173.

[9 ] An algorithm of constructing maximal consistent block [link ]
     Jiye Liang, Baoli Wang, Yuhua Qian, Deyu Li
      International Journal of Computer Science and Knowledge Engineering , 2008, 2(1): 11-18.

[10 ] Positive approximation and rule extracting in incomplete information systems [link ]
     Yuhua Qian, Jiye Liang
      International Journal of Computer Science and Knowledge Engineering , 2008, 2(1): 51-63.

[11 ] Measures for evaluating the decision performance of a decision table in rough set theory [link ]
     Yuhua Qian, Jiye Liang, Deyu Li, Haiyun Zhang, Chuangyin Dang
      Information Sciences , 2008, 178(1): 181-202.

[12 ] Uncertainty measure of rough sets based on a knowledge granulation of incomplete information systems [link ]
     Junhong Wang, Jiye Liang, Yuhua Qian, Chuangyin Dang
      International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 2008, 16(2): 233-244.

[13 ] Combination Entropy & Combination Granulation in Rough Set Theory [link ]
     Yuhua Qian, Jiye Liang
      International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems , 2008, 16(2): 179-193.

[14 ] On the evaluation of the decision performance of an incomplete decision table [link ]
     Yuhua Qian, Jiye Liang, Chuangyin Dang, Haiyun Zhang, Jianmin Ma
      Data & Knowledge Engineering , 2008, 65(3):373-400.

[15 ] An incremental approach to computation of a core based on conditional entropy [link ]
    Jiye Liang, Wei Wei, Yuhua Qian
      Systems Engineering-Theory & Practice , 2008, 4: 81-89.

[16 ] Research on fuzzy integrative evaluation for implemented situation of technological projects [link ]
    Jiye Liang, Chengyuan Zhu, Jianlong Hu, Deyu Li
      Journal of Systems Engineering , 2008, 23(5): 636-640.

[17 ] Converse approximation and rule extracting from decision tables in rough set theory [link ]
     Yuhua Qian, Jiye Liang, Chuangyin Dang
      Computers & Mathematics with Applications , 2008, 55: 1754-1765.

[18 ] Consistency measure, inclusion degree and fuzzy measure in decision tables [link ]
     Yuhua Qian, Jiye Liang, Chuangyin Dang
      Fuzzy Sets and Systems , 2008, 159: 2353-2377.

[19 ] Interval ordered information systems [link ]
     Yuhua Qian, Jiye Liang, Chuangyin Dang
      Computers & Mathematics with Applications , 2008, 56: 1994-2009.

[20 ] Information granules and entropy theory in information systems [link ]
    Jiye Liang, Yuhua Qian
      Science in China, Series F: Information Sciences , 2008, 51(10): 1427-1444.

[6 ] Consistency and fuzziness in ordered decision tables [link ]
     Yuhua Qian, Jiye Liang, Wei Wei, Feng Wang
     RSKT , 2008: 63-71.

[7 ] Change mechanism of a decision table's decision performance caused by attribute reductions [link ]
      Wei Wei, Jiye Liang, Yuhua Qian
      GrC , 2008: 638-643.

[8 ] Granulation operators on a knowledge base [link ]
     Yuhua Qian, Jiye Liang, Wei Wei
      GrC , 2008: 538-543.

[3 ] Study of decision implications based on formal concept analysis [link ]
     Kaishe Qu, Yanhui Zhai, Jiye Liang
     International Journal of General Systems , 2007, 36(2), 147-156.

[4 ] Representation and extension of rough set theory Based on formal concept analysis [link ]
     Kaishe Qu, Yanhui Zhai, Jiye Liang, Deyu Li
     Journal of Software , 2007, 18(9): 2174-2182.

[5 ] Knowledge distance in information systems [link ]
     Yuhua Qian，Chuangyin Dang, Jiye Liang, Feng Wang, Wei Xu
     Journal of System Sciences and System Engineering , 2007, 16(4): 434-449.

[1 ] Evaluation method for decision rule sets [link ]
      Yuhua Qian, Jiye Liang
      RSFDGrC , 2007, pp. 272-279.

[2 ] MGRS in incomplete information systems [link ]
      Yuhua Qian, Jiye Liang, Chuangyin Dang
      IEEE International Conference on Granular Computing , 2007, pp. 163-163.

[1] The information entropy, rough entropy and knowledge granulation in incomplete information systems [link]
      Jiye Liang, Zhongzhi Shi, Deyu Li, M. J. Wireman
      International Journal of General Systems, vol. 34, 2006.

[1] A measure method for indiscernibility in imperfect information system [link]
      Jiye Liang, Jifang Pang
      JCIS, 2006.

[2] Combination entropy and combination granulation in incomplete information system [link]
      Yuhua Qian, Jiye Liang
      RSKT, 2006, pp. 184-190.

[1] Rough set approximation based on dynamic granulation [link]
     Jiye Liang, Yuhua Qian, Chengyuan Chu, Deyu Li, Junhong Wang
     RSFDGrC, 2005, pp. 701-708.

[1] The information entropy, rough entropy and knowledge granulation in rough set theory [link]
      Jiye Liang, Zhongzhi Shi
      International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12 (1) : 37-46.

[2] The algebraic properties of concept lattice [link]
      Kaishe Qu, Jiye Liang, Junhong Wang, Zhongzhi Shi
      Journal of Systems Science and Information, 2004, 3 (2): 36-47.

[1] Applications of inclusion degree in rough set theory [link]
     Jiye Liang, Zhongzhi Shi, Deyu Li
      International Journal of Computational Cognition, 2003, 1 (2): 67-78.

[1] Rough set data analysis algorithms for incomplete information systems [link]
      K. S. Chin, Jiye Liang, Chuangyin Dang
      RSFDGrC, 2003: 264-268.