Data Science Lab

Longbing Cao. Behavior Analytics: Methods and Applications, AAAI2019 tutorial

AAAI2019 tutorial slides: Behavior analytics: Modeling and applications

Outline

Complex behaviors are widely seen in artificial and natural intelligent systems, on the internet, physical and virtual systems, social and online networks, multi-agent systems, and brain systems. The in-depth understanding of complex behaviors has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues. However, traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. The so-called behavior analysis in data analytics and learning often focuses on human demographic and business usage data, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle, dynamics and impact on complex problems and business issues. In this tutorial, we will present an overview of behavior analytics, review and discuss state-of-the-art and newly emerged techniques for complex behavior analytics, which cover high impact behavior sequence analysis, impact-oriented combined behavior analysis, high utility behavior analysis, nonoccurring behavior analysis, coupled/group/collective behavior analysis, statistical modeling of coupled behaviors, probabilistic modeling of sparse rating behaviors, understanding behavior choice and attraction, behavior analysis with recurrent networks, behavior analysis in visual data, behavior learning from demonstrations. We will show that in-depth behavior analytics creates new opportunities, directions and means for learning and analysis of complex behaviors in both physical and virtual organizations.

About Lecturer

Professor Longbing Cao holds a PhD in Pattern Recognition and Intelligent Systems in Chinese Academy of Sciences and another PhD in Computing Science at UTS. He has published some 300 publications, four monographs, and four edited books in recent 15 years. He has been working on data science and analytics research, education, development, and enterprise applications since he was a CTO and then joined UTS. Motivated by real-world significant and common challenges, he has been leading the team to develop theories, tools and applications for new areas including non-IID learning, actionable knowledge discovery, behavior informatics, and complex intelligent systems, in addition to issues generally concerned in artificial intelligence, knowledge discovery, machine learning, and their enterprise applications. In data science and analytics, he initiated the Data Science and Knowledge Discovery lab at UTS in 2007, the Advanced Analytics Institute in 2011, the degrees Master of Analytics (Research) and PhD in Analytics in 2011 which are recognized as the world first degrees in data science, the IEEE Task Force on Data Science and Advanced Analytics (DSAA) and IEEE Task Force on Behavior, Economic and Soci-cultural Computing in 2013, the IEEE Conference on Data Science and Advanced Analytics (DSAA), the ACM SIGKDD Australia and New Zealand Chapter in 2014, and the International Journal of Data Science and Analytics with Springer in 2015. He served as program and general chairs of conferences such as KDD2015. In enterprise data science innovation, his team has successfully delivered many large projects for government and business organizations in over 10 domains including finance/capital markets, banking, health and car insurance, health, telco, recommendation, online business, education, and the public sector including ATO, DFS, DHS, DIBP and IP Australia, resulting in billions of dollar savings and mentions in government, industry, media and OECD reports. In 2013, AAI was the only organization specially mentioned in the Governments first big data paper: Big Data Strategy Issues Paper. He has been delivered invited and keynote speeches to over 20 conferences, guest lectures and seminars to many universities, and tutorials to conferences including AAAI, IJCAI and KDD.

More information about the presenter and the Data Science Lab can be accessible from www.datasciences.org.

Content

The tutorial will include the following contents (adjustment may be made further if accepted):

Overview of Behavior Informatics: the qualitative analytics addresses the task of behavior reasoning and verification, while the quantitative research targets behavior learning, analysis and evaluation;
What is Behavior: we present an abstract behavior model, which captures both intrinsic and contextual properties of behaviors from both subjective and objective perspectives, and an overview of behavior informatics;
High Impact and High Utility Behavior Analysis: algorithms and case studies are discussed to identify behaviors associated with high impact and utility;
Nonoccurring Behavior Analysis: algorithms and case studies are explored about mining negative behavior sequences in an efficient way;
Coupled/Group/Collective Behavior Analysis: algorithms and case studies are provided for identifying and analyzing group/community behaviors and anomalies;
Statistical Modeling of Coupled Behaviors: statistical models are presented to induce explicit and implicit couplings between attributes into statistical models for modeling behaviors;
Understanding Behavior Intent: choice modeling, attraction learning and deep models will be introduced to learn user choice and attraction and sequential interactions;
Behavior Analysis with Recurrent Networks: we will introduce how deep recurrent networks are used to address behavior sequence analysis problems using several representative cases;
Behavior Analysis in Visual Data: traditional approaches and recently emerged deep learning-based approaches for learning visual behaviors are introduced;
Behavior Learning from Demonstrations: we will introduce several advanced imitation learning approaches,which focus on imitating human behaviors in a set of demonstrations data;
Challenges and Prospects: open issues and potential are discussed for complex behavior modeling, analysis and mining in terms of both qualitative and quantitative aspects.

Important references

We here list some references that are related to this tutorial (adjustment will be made further before the tutorial delivery):

Longbing Cao, Philip S Yu (Eds). Behavior Computing: Modeling, Analysis, Mining and Decision, Springer, 2012.
Longbing Cao. Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management. KDD2017 (Industry invited talks), 2017.
Herath, S., Harandi, M., Porikli, F. (2017). Going deeper into action recognition: A survey. Image and vision computing, 60, 4-21.
Hussein, A., Gaber, M. M., Elyan, E., Jayne, C. (2017). Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR), 50(2), 21.
Longbing Cao, Xiangjun Dong and Zhigang Zheng. e-NSP: Efficient Negative Sequential Pattern Mining. Artificial Intelligence, 235: 156-182, http://dx.doi.org/10.1016/j.artint.2016.03.001, 2016.
Zhigang Zheng,WeiWei, Chunming Liu,Wei Cao, Longbing Cao, Maninder Bhatia. An effective contrast sequential pattern mining approach to taxpayer behavior analysis, World Wide Web 19(4): 633-651 (2016).
Longbing Cao. Coupling Learning of Complex Interactions, Information Processing and Management, 51(2):167-186 (2015).
Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao. Mining actionable combined patterns of high utility and frequency. DSAA 2015: 1-10.
Can Wang, Longbing Cao, Chi-Hung Chi. Formalization and Verification of Group Behavior Interactions. IEEE T. Systems, Man, and Cybernetics: Systems 45(8): 1109-1124 (2015).
Longbing Cao, Philip S. Yu, Vipin Kumar. Nonoccurring Behavior Analytics: A New Area. IEEE Intelligent Systems 30(6): 4-11 (2015).
Longbing Cao. Behavior Informatics: A New Perspective. IEEE Intelligent Systems (Trends and Controversies), 29(4): 62-80, 2014.
Longbing Cao and Thorsten Joachims. Behavior Computing, IEEE Intelligent Systems, 29(4): 62-66, 2014.
Longbing Cao. Combined Mining: Analyzing Object and Pattern Relations for Discovering and Constructing Complex but Actionable Patterns, WIREs Data Mining and Knowledge Discovery, 3(2): 140-155, 2013.
Junfu Yin, Zhigang Zheng, Longbing Cao, Yin Song,Wei Wei. Efficiently Mining Top-K High Utility Sequential Patterns, ICDM2013: 1259-1264.
Junfu Yin, Zhigang Zheng, Longbing Cao. USpan: An Efficient Algorithm for Mining High Utility Sequential Patterns, KDD 2012, 660-668.
Cao, L., Ou, Y., Yu, P.S. Coupled Behavior Analysis with Applications, IEEE Transactions on Knowledge and Data Engineering, 24 (8): 1378-1392, 2012.
Yin, J., Zheng, Z., Cao, L. USpan: An Efficient Algorithm for Mining High Utility Sequential Patterns, KDD 2012, 660-668, 2012.
Song, Y., Cao, L. et al. Coupled Behavior Analysis for Capturing Coupling Relationships in Group-based Market Manipulations. KDD 2012, 976-984, 2012.
Yin Song and Longbing Cao. Graph-based Coupled Behavior Analysis: A Case Study on Detecting Collaborative Manipulations in Stock Markets, IJCNN 2012, 1-8, 2012.
Wang, H., Klser, A., Schmid, C., Liu, C. L. (2011). Action recognition by dense trajectories. In CVPR (pp.3169-3176).
Longbing Cao, Yuming Ou, Philip S YU, Gang Wei. Detecting Abnormal Coupled Sequences and Sequence Changes in Group-based Manipulative Trading Behaviors, KDD 2010, 85-94.
Longbing Cao, In-depth Behavior Understanding and Use: the Behavior Informatics Approach, Information Science, 180(17); 3067-3085, 2010.
Zhigang Zheng, Yanchang Zhao, Ziye Zuo, Longbing Cao, Huaifeng Zhang, Yanchang Zhao, Chengqi Zhang. An Efficient GA-Based Algorithm for Mining Negative Sequential Patterns, PAKDD 2010, 262-273.
Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Jian Pei, Shanshan Wu, Chengqi Zhang and Hans Bohlscheid, Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns, ECMLPKDD 2009, 648-663, 2009.
Yanchang Zhao, Huaifeng Zhang, Longbing Cao, Chengqi Zhang and Hans Bohlscheid. Mining Both Positive and Negative Impact-Oriented Sequential Rules From Transactional Data, PAKDD 2009, 656-663.
Yeffet, L., Lior W. (2009). Local trinary patterns for human action recognition. In CVPR (pp. 492-497).
Yanchang Zhao, Huaifeng Zhang, Longbing Cao, and Chengqi Zhang, Efficient Mining of Event-Targeted Negative Sequential Rules, WI 2008, 336-342.
Longbing Cao, Yanchang Zhao, Chengqi Zhang. Mining Impact-Targeted Activity Patterns in Imbalanced Data, IEEE Trans. on Knowledge and Data Engineering, 20(8): 1053-1066, 2008.
Longbing Cao, Yanchang Zhao, Chengqi Zhang, Huaifeng Zhang. Activity Mining: from Activities to Actions, International Journal of Information Technology Decision Making, 7(2): 259-273, 2008.
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B. (2008). Learning realistic human actions from movies. In CVPR (pp. 1-8).

Related tutorials

KDD2018 tutorial: Behavior analytics: Methods and applications

IJCAI2013 tutorial: Behavior informatics – Modeling, analysis and mining of complex behaviors