Jiuyong Li

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Main Publications

Rule and relationship discovery

J. Li, S. Ma, T. Le, L. Liu, and J. Liu, Causal Decision Trees , IEEE Transactions on Knowledge and Data Engineering, 29(2), 257-271, 2017.
J. Li, T. Le, J. Liu, J. Liu, J. Zhou, B. Sun, and S. Ma, From Observational Studies to Causal Rule Mining , ACM Transactions on Intelligent Systems and Technology, 7(2), 14:1-17, 2016.
J. Li, L. Liu, T. Le, Practical approaches to causal relationship exploration , SpringerBriefs in Electrical and Computer Engineering , Springer 2015.
J. Li, J. Liu, H. Toivonen, K. Satou, Y. Sun, and B. Sun, Discovering statistically non-redundant subgroups, Knowledge-Based Systems, 67, 315-327, 2014. (Program is available in my software section.)
J. Li, J. Liu, H. Toivonen, and J. Yong, Effective pruning for the discovery of conditional functional dependencies, The Computer Journal, 56(3), 378-392, 2013.
J. Li, From association analysis to causal discovery, Machine Learning and Sensory Data Analysis, 1-2, 2013.
J. Li, T. Le, L. Liu, J. Liu, Z. Zhou, and B. Sun, Mining causal association rules, Proceedings of ICDM Workshop on Causal Discovery (CD), 114-123, 2013, IEEE CS Press.
Z. Jin, J. Li, L. Liu, T. Le, B. Sun, and R. Wang, Discovery of causal rules using partial association, Proceedings of IEEE International Conference on Data Mining (ICDM), 309-318, 2012, IEEE CS Press.
J. Liu, J. Li, C. Liu and Y. Chen, Discover dependencies from data - a review, IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(2), 251-264, 2012.
J. Li, A. Fu, and P. Fahey, Efficient discovery of risk patterns in medical data, Artificial Intelligence in Medicine, 45, 77-89, 2009.
J. Li, On optimal rule discovery, IEEE Transactions on Knowledge and Data Engineering, 18 (4), 2006, 460-471.
J. Li, A. Fu, H. He, J. Chen, H. Jin, D. McAullay, G. Williams, R. Sparks, C. Kelman, Mining risk patterns in medical data, In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 770 – 775, 2005, ACM Press.
J. Li, H. Shen and R. Topor, Mining the informative rule set for prediction, Journal of Intelligent Information Systems, 22:2, 155-174, 2004.
J. Li and Y. Zhang, Direct interesting rule generation, In Proceedings of IEEE International Conference on Data Mining (ICDM), 155 - 162, 2003.
J. Li, H. Shen and R. Topor, Mining the optimal class association rule set, Knowledge-based Systems, 15(7), 399 -405, 2002.
J. Li, H. Shen and R. Topor, Mining the smallest association rule set for predictions, In Proceedings of IEEE international conference on data mining (ICDM), 361 - 368, 2001.
J. Li, H. Shen and R. Topor, An adaptive method of numerical attribute merging for quantitative association rule mining, In Proceedings of the 5th international computer science conference (ICSC), 41 - 50, LNCS 1749, 1999, Springer.

Classification and prediction

Y. Sun, J. Li, J. Liu, C. Chow, B. Sun, and R. Wang, Using causal discovery for feature selection in multivariate numerical time series, Machine Learning, in press, 2014.
Y. Sun, J. Li, J. Liu, B. Sun and C. Chow, An improvement of symbolic aggregate approximation distance measure for time series, Neurocomputing, 138, 189-198, 2014.
J. Li, Robust rule based predictions, IEEE Transactions on Knowledge and Data Engineering, 18(8), 2006, 1043-1054.
J. Li, J. Jones, Using multiple and negative target rules to make classifier more understandable, Knowledge-based Systems, 19(6), 2006.
F. Khalil, J. Li, and H. Wang, An integrated model for next page access prediction, International Journal of Knowledge and Web Intelligence, 1(1/2), 48 – 80, 2009.
H. Hu and J. Li, Using association rules to make rule-based classifiers robust, In Proceedings of Sixteenth Australasian Database Conference (ADC), 47-52, 2005, ACS Press.
J. Li, R. Topor and H. Shen, Construct robust rule sets for classification, In Proceedings of the Eighth ACMKDD International Conference on Knowledge Discovery and Data Mining (KDD), 564 -569, 2002, ACM Press.
H. Hu, J. Li, H. Wang, G. Daggard and M. Shi, A maximally diversified multiple decision tree algorithm for microarray data classification, In Proceedings first Workshop on Intelligent Systems for Bioinformatics, 2006.
H. Hu, J. Li, A. Plank, H. Wang, and G. Daggard, A comparative study of classification methods for microarray data analysis , In Proceedings of Australian Data Mining conference, 31 - 35, 2006, ACS Press.

Privacy Preservation and discrimination

J. Li, J. Liu, L. Liu, T. Le, S. Ma, and Y. Han, Discrimination detection by causal effect estimation, IEEE International Conference on Big Data, 2017..
S. Sattar, J. Li, J. Liu, R. Heatherly and B. Malin A probabilistic approach to mitigate composition attacks on privacy in non-coordinated environments, Knowledge-based Systems, 67, 361-372, 2014.
S. Sattar, J. Li, X. Ding, J. Liu, and M. Vincent A general framework for privacy preserving data publishing, Knowledge-based Systems, 54, 276-287, 2013.
X. Ding, Q. Yu, J. Li, J. Liu, and H. Jin Distributed Anonymization for Multiple Data Providers in a Cloud System, International Conference on Database Systems for Advanced Applications (DASFAA), 346-360, 2013.
X. Sun, H. Wang, J. Li and Y. Zhang, Satisfying privacy requirements before data anonymization, The Computer Journal, 55(4), 422-437, 2012.
M. M. Baig, J. Li, J. Liu, X. Ding, and H. Wang, Data privacy against composition attack, International Conference on Database Systems for Advanced Applications (DASFAA), 320-334, 2012.
J. Li, J. Liu, M. Baig and R. Wong, Information based data anonymization for classification utility, Data and Knowledge Engineering, 70(12), 1030-1045, 2011 (implementation).
X. Sun, H. Wang, J. Li and J. Pei, Publishing anonymous survey rating data, Data Mining and Knowledge Discovery, 23(3), 379-406, 2011.
M. Baig, J. Li, J. Liu and H. Wang, Cloning for privacy protection in multiple independent data publications, In Proceedings of ACM Conference on Information and Knowledge Management (CIKM), 2011
R. Wong, J. Li, A. Fu, and K. Wang, (alpha, k)-anonymous data publishing, Journal of Intelligent Information Systems, 33(2) 209-234, 2009.
X Sun, H Wang, J. Li, T. M. Truta, Enhanced p-sensitive k-anonymity models for privacy preserving data publishing, Transactions on Data Privacy, 1(2): 53-66 2008
J. Li, R. Wong, A. Fu, J. Pei, Anonymisation by local recoding in data with hierarchical taxonomies, IEEE Transactions on Knowledge and Data Engineering, 20(8), 2008. 1181-1194.
J. Li, H Wang, Huidong Jin, and Jianming Yong, Current developments of k-Anonymous data releasing, Electronic Journal of Health Informatics, 3(1), 2008.
R. Wong, J. Li, A. Fu, K. Wang, (alpha, k)-anonymity: an enhanced k-anonymity model for privacy-preserving data publishing, In Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining (KDD), 754-759, 2006.
J. Li, R. Wong, A.Fu, J. Pei, Achieving k-Anonymity by clustering in attribute hierarchical structures, In Proceedings of 8th International Conference on Data Warehousing and Knowledge Discovery, 405-416, 2006, Springer.

Applications (medical, bioinformatics, and web access prediction)

T. Le, L. Liu, J. Zhang, B. Liu, and J. Li, From miRNA regulation to miRNA - TF co-regulation: computational approaches and challenges , Briefings in Bioinformatics, In press, 2014.
J. Zhang, T. Le, L. Liu, B. Liu, J. He, G. J. Goodall, and J. Li, Identifying direct miRNA–mRNA causal regulatory relationships in heterogeneous data, Journal of Biomedical Informatics, In press, 2014.
J. Zhang, T. Le, L. Liu, B. Liu, J. He, G. J. Goodall, and J. Li, Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data, Bioinformatics, 30(21), 3070-3077, 2014. (Supplementary files)
T. Le, L. Liu, A. Tsykin, G. J. Goodall, B. Liu, B. Sun, and J. Li, Inferring microRNA-mRNA causal regulatory relationships from expression data, Bioinformatics, 29(6), 765-771, 2013. (Supplementary files)
T. Le, L. Liu, B. Liu, A. Tsykin, G. J. Goodall, Kenji Satou, and J. Li, Inferring microRNA and transcription factor regulatory networks in heterogeneous data, BMC Bioinformatics, 14, 92, 2013.
B. Li, J. Li, and M. Carins, Identifying miRNAs, targets and functions, Briefings in Bioinformatics, 15(1), 1-19, 2014.
B. Liu, L. Liu, A. Tsykin, G. Goodall, J. Green, M. Zhu, C. Kim and J. Li, Identifying functional miRNA-mRNA regulatory modules with correspondence latent Dirichlet allocation, Bioinformatics, 26(24), 3105-3111, 2010.
B. Liu, J. Li, A, Tsykin, L. Liu, A. B. Gaur and G. J. Goodall, Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy, BMC Bioinformatics, 2009 (10), 408.
B Liu, J Li, A Tsykin, Discovery of functional miRNA-mRNA regulatory modules with computational methods, Journal of Biomedical Informatics, 42(4), 685-691, 2009.
J. Li, A. Fu, and P. Fahey, Efficient discovery of risk patterns in medical data, Artificial Intelligence in Medicine, 45, 77-89, 2009.
J. Li, A. Fu, H. He, J. Chen, H. Jin, D. McAullay, G. Williams, R. Sparks, C. Kelman,Mining risk patterns in medical data, In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 770 – 775, 2005, ACM Press.
J. Chen, H. He, J. Li, H. Jin, D. McAullay, G. Williams, R. Sparks, C. Kelman, Representing association classification rules mined from health data, Knowledge Based Intelligent Systems for Healthcare in KES, 1225-1231, 2005.
H. He, H. Jin, J. Chen, D. McAullay, J. Li, T. Fallon, Analysis of breast feeding data using data mining methods, In Proceedings of Australian data mining conference (AusDM), 43 -48, 2006, ACS press.
F. Khalil, J. Li, and H Wang, Integrating recommendation models for improved Web page prediction accuracy, In Proceedings of the Thirty-First Australasian Computer Science Conference, Wollongong, 2008.
F. Khalil, J. Li and, H. Wang, Integrating markov model with clustering for predicting Web page accesses, In Proceedings of Australian World Wide Conference, 2007.
F. Khalil, J. Li, H. Wang, A framework of combining Markov model with association rules for predicting Web page accesses, In Proceedings of Australian Data Mining Conference, 166 -173, 2006, ACS Press
H. Hu, J. Li, H. Wang, G. Daggard and M. Shi, A maximally diversified multiple decision tree algorithm for microarray data classification, In Proceedings of the first Workshop on Intelligent Systems for Bioinformatics.2006.
H. Hu, J. Li, H. Wang, and G. Daggard, Combined gene selection methods for microarray data analysis, In Proceedings of 10th International Conference Knowledge-Based Intelligent Information and Engineering Systems, (KES), 976--983, 2006, LNAI 4251, 2006, Springer.
H. Hu, J. Li, A. Plank, H. Wang, and G. Daggard, A comparative study of classification methods for microarray data Analysis, In Proceedings of Australian Data Mining conference, 31-35, 2006, ACS Press.

Unsupervised learning

H. Li, J. Li, L. Liu, J. Liu, I. Lee, and J. Zhao, Exploring Groups from Heterogeneous Data via Sparse Learning, In Proceedings of Seventeenth Pacific-Asian Conference in Knowledge Discovery and Data Mining (PAKDD), 556-567, 2013.
J. Li, X. Huang, C. Selke, J. Yong, A fast algorithm for finding correlation clusters in noise data, In Proceedings of Eleventh Pacific-Asian Conference in Knowledge Discovery and Data Mining (PAKDD), 639-647, 2007.
X. Chen, J. Li, G. Daggard, X. Huang, Finding similar patterns in Microarray data, In Proceedings of Australian Conference on Artificial Intelligence (AI), 1272-1276, 2005.
F. Khalil, J. Li, and H Wang, Integrating recommendation models for improved Web page prediction accuracy, In Proceedings of the Thirty-First Australasian Computer Science Conference, Wollongong, 2008.
F. Khalil, J. Li and, H. Wang, Integrating markov model with clustering for predicting Web age accesses, In Proceedings of Australian World Wide Conference, 2007.

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My Software

Causal Decision Tree implementation
Causal Relationship Exploration (CRE)
Mining risk and preventive patterns
Robust rule based classification
Diversified Multiple Tree Classifier
Conditional Functional Dependency Discovery
Statistically non-redundant Subgroup Discovery
Discrimination detection by combining association rules and potential outcome model

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Last Revised: January 2018