Publications

Journal ArticlesInproceedingsWorkshops
  1. Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong. Detecting Pickpocket Suspects from Large-Scale Public Transit Records. IEEE Transactions on Knowledge and Data Engineering, March 2019.
  2. Bowen Du, Wenjun Zhou, Chuanren Liu, Yifeng Cui, Hui Xiong. Transit Pattern Detection Using Tensor Factorization. INFORMS Journal on Computing, February 2019.
  3. Sun, Leilei and Jin, Bo and Yang, Haoyu and Tong, Jianing and Liu, Chuanren and Xiong, Hui. Unsupervised EEG feature extraction based on echo state network. Information Sciences, February 2019.
  4. Guifeng Wang, Qi Liu, Hongke Zhao, Chuanren Liu, Tong Xu, Enhong Chen. Product Supply Optimization for Crowdfunding Campaigns. IEEE Transactions on Big Data, Accepted, December 2018.
  5. Jingyuan Yang, Chuanren Liu, Mingfei Teng, Ji Chen, Hui Xiong. A Unified View of Social and Temporal Modeling for B2B Marketing Campaign Recommendation. IEEE Transactions on Knowledge and Data Engineering, May 2018.
  6. Hao Zhong, Chuanren Liu (Corresponding Author), Junwei Zhong, Hui Xiong. Which Startup to Invest in: A Personalized Portfolio Strategy. Annals of Operations Research, April 2018.
  7. Keli Xiao, Qi Liu, Chuanren Liu, Hui Xiong. Price shock detection with an influence-based model of social attention. ACM Transactions on Management Information Systems, October 2017.
  8. Chuanren Liu, Hui Xiong, Spiros Papadimitriou, Yong Ge, Keli Xiao. A Proactive Workflow Model for Healthcare Operation and Management. IEEE Transactions on Knowledge and Data Engineering, Nov 2016.
  9. Leilei Sun, Chonghui Guo, Chuanren Liu, Hui Xiong. Fast Affinity Propagation Clustering based on Incomplete Similarity Matrix. Knowledge and Information Systems, Aug 2016.
  10. Yanchi Liu; Chuanren Liu; Nicholas Jing Yuan; Lian Duan; Yanjie Fu; Hui Xiong; Songhua Xu; Junjie Wu. Intelligent Bus Routing with Heterogeneous Human Mobility Patterns. Knowledge and Information Systems, Feb 2016.
  11. Chuanren Liu, Kai Zhang, Hui Xiong, Guofei Jiang, Qiang Yang. Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization. IEEE Transactions on Knowledge and Data Engineering, 28(1):211-223, Jan 2016.Sequential pattern analysis aims at finding statistically relevant temporal structures in sequences. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode the sequential values. This is so-called "curse of cardinality", which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, we propose a "temporal skeletonization" approach to proactively reduce the cardinality of the representation for sequences by uncovering significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph, and use the "skeleton" of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. As a consequence, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.
  12. Yanhong Guo, Wenjun Zhou, Chunyu Luo, Chuanren Liu, Hui Xiong. Instance-Based Credit Risk Assessment for Investment Decisions in P2P Lending. European Journal of Operational Research, 249(2):417-426, March 2016.
  13. Hengshu Zhu, Chuanren Liu, Yong Ge, Hui Xiong, Enhong Chen. Popularity Modeling for Mobile Apps: A Sequential Approach. IEEE Transactions on Cybernetics, 45(7):1303-1314, July 2015.
  14. Tianming Hu, Chuanren Liu, Jing Sun, Hui Xiong, Sam Yuan Sung, Peter A. Ng. High Dimensional Clustering: A Clique Based Hypergraph Partitioning Framework. Knowledge and Information Systems, 39(1):61-88, April 2014.
  15. Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang. Exploiting Cognitive Structure for Adaptive Learning Recommender Systems. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
  16. Bo Jin, Haoyu Yang, Leilei Sun, Chuanren Liu, Yue Qu, Jianing Tong. A Treatment Engine by Predicting Next-Period Prescriptions. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1608-1616, 2018.
  17. Jingyuan Yang, Chuanren Liu, Mingfei Teng, Hui Xiong. Exploiting Temporal and Social Factors for B2B Marketing Campaign Recommendations. The 15th IEEE International Conference on Data Mining (ICDM 2015), Regular paper:499 - 508, 2015.Business to Business (B2B) marketing aims at meeting the needs of other businesses instead of individual consumers. In B2B markets, the buying processes usually involve series of different marketing campaigns providing necessary information to multiple decision makers with different interests and motivations. The dynamic and complex nature of these processes imposes significant challenges to analyze the process logs for improving the B2B marketing practice. In this paper, to develop a marketing campaign recommender system, we first propose the temporal correlation graph as the temporal knowledge representation of the buying process of each business customer. We then develop the low-rank graph reconstruction framework to identify the common graph patterns and predict the missing edges in the temporal correlation graphs. We show that the prediction of the missing edges is effective to recommend the marketing campaigns to the business customers during their buying processes. Moreover, we also exploit the community relationships of the business customers to improve the performances of the graph edge predictions and the marketing campaign recommendations. Finally, empirical studies on real-world B2B marketing data sets show that the proposed method improve the quality of the campaign recommendations for challenging B2B marketing tasks.
  18. Qi Liu, Xianyu Zeng, Chuanren Liu, Hengshu Zhu, Enhong Chen, Hui Xiong, Xing Xie. Mining Indecisiveness in Customer Behaviors. The 15th IEEE International Conference on Data Mining (ICDM 2015), Regular paper:705-714, 2015.In the retail market, the consumers' indecisiveness refers to the inability to make quick and assertive decisions when they choose between competing products. Indeed, indecisiveness has been investigated in a number of fields, such as economics and psychology. However, these studies are usually based on the subjective customer survey data with some manually defined questions. Instead, this paper provides a focused study on automatically mining indecisiveness in massive customer behaviors in online stores. Specifically, we first give a general definition to measure the observed indecisiveness from each behavior session. From these observed indecisiveness, we can learn the latent factors/reasons by a probabilistic factor-based model. These two factors are the indecisive indexes of the customers and the product bundles, respectively. Next, we demonstrate that this indecisiveness mining process could be useful in several potential applications, such as the competitive product detection and personalized product bundles recommendation. Finally, we perform extensive experiments on a large-scale behavioral logs of online customers in a distributed environment. The results reveal that our measurement of indecisiveness agrees with the common sense assessment, and the discoveries are useful in predicting customer behaviors and providing better recommendation services for both customers and online retailers.
  19. Chuanren Liu, Fei Wang, Jianying Hu, Hui Xiong. Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework. The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), Research track:705-714, 2015.
  20. Chuanren Liu, Kai Zhang, Hui Xiong, Guofei Jiang, Qiang Yang. Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), Research track:1336-1345, 2014.Demo
  21. Chuanren Liu, Yong Ge, Hui Xiong, Keli Xiao, Wei Geng, Matt Perkins. Proactive Workflow Modeling by Stochastic Processes with Application to Healthcare Operation and Management. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), Industry track:1593-1602, 2014.
  22. Chuanren Liu, Tianming Hu, Yong Ge, Hui Xiong. Finding Well-Clusterable Subspaces for High Dimensional Data: A Numerical One-Dimension Approach. The 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014), 311-323, 2014.
  23. Qi Liu, Zheng Dong, Chuanren Liu, Enhong Chen, Xing Xie. Social Marketing Meets Targeted Cus- tomers: A Typical User Selection and Coverage Perspective. The 14th IEEE International Conference on Data Mining (ICDM 2014), 350-359, 2014.
  24. Yanchi Liu, Chuanren Liu, Jing Yuan, Lian Duan, Yanjie Fu, Hui Xiong. Exploiting Heterogeneous Human Mobility Patterns for Intelligent Bus Routing. The 14th IEEE International Conference on Data Mining (ICDM 2014), 360-369, 2014.
  25. Chuanren Liu, Jianjun Xie, Yong Ge, Hui Xiong. Stochastic Unsupervised Learning on Unlabeled Data. Journal of Machine Learning Research, W&CP(27):111-122, 2012.
  26. Chuanren Liu, Hui Xiong, Yong Ge, Wei Geng, Matt Perkins. A Stochastic Model for Context-Aware Anomaly Detection in Indoor Location Traces. The 12th IEEE International Conference on Data Mining (ICDM 2012), 449-458, 2012.
  27. Chuanren Liu, Tianming Hu, Yong Ge, and Hui Xiong. Which Distance Metric is Right: An Evolutionary K-Means View. The 12th SIAM International Conference on Data Mining (SDM 2012), 907-918, 2012.
  28. Yong Ge, Hui Xiong, Chuanren Liu, and Zhihua Zhou. A Taxi Driving Fraud Detection System. The 11th IEEE International Conference on Data Mining (ICDM 2011), 181-190, 2011.
  29. Yong Ge, Chuanren Liu, Hui Xiong, Jian Chen. A taxi business intelligent system. The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), Demonstration track:735-738, 2011.
  30. Tianming Hu, Chuanren Liu, Jing Sun, Sam Yuan Sung, and Peter A. Ng. Pairwise Constrained Clustering with Group Similarity-Based Patterns. The 9th International Conference on Machine Learning and Applications (ICMLA 2010), 260-265, 2010.
  31. Tianming Hu, Ji Ouyang, Chao Qu, and Chuanren Liu. Initialization of the Neighborhood EM Algorithm for Spatial Clustering. The 5th International Conference on Advanced Data Mining and Applications (ADMA 2009), 487-495, 2009.
  32. Jin Fang, Chuanren Liu, Yi Zhu. Sequence Clustering with Temporal Graph and Sequence Recovery. The 2018 INFORMS Workshop on Data Science, November 2018.
  33. Yuyue Chen, Chuanren Liu. Aggregation and Disaggregation of Information: A Holistic View. Doctoral Student Forum in the 2017 IEEE International Conference on Data Mining, November 2017.
  34. Chuanren Liu, Hui Xiong. Data Mining for Enhancement of Health Care Management. Doctoral Student Forum in the 2012 SIAM International Conference on Data Mining, April 2012.