IJCAI 2019

Abstract

Learning of complex data and behaviors is increasingly becoming a major challenge and opportunity for big business and innovative learning theories and tools. Some of the most critical challenges of learning of complex data and behaviors are the uncovering of the explicit and implicit coupling relationships embedded in mixed heterogeneous data from single/multiple sources. The couplings and heterogeneities of the non-IIDness aspects form the essence of complex data and behaviors and most real-world applications, namely the data is non-IID.

Most of classic theoretical systems and tools in statistics, data mining, database, knowledge management and machine learning assume the independence and identical distribution of underlying objects, features and values. Such theories and tools may lead to misleading or incorrect understanding of real-life data complexities. Non-IID learning of complex data and behaviors is a foundational theoretical problem in AI and data science, which considers the couplings and heterogeneities between entities, properties, interactions and contexts. In this tutorial, we present a comprehensive tutorial of non-IID learning. We begin with the limitation of IID learning in handling complex data and behaviors and introduce abstract learning model and representation for non-IID learning, and then present frameworks and algorithms for non-IID metric learning, classification, clustering, ensemble clustering, outlier detection, feature selection, recommender systems, and text mining, and finally discuss open challenges and prospects.

Lecturers

Longbing Cao is a professor of information technology at the University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Pattern Recognition and Intelligent Systems in Chinese Academy of Sciences and another Ph.D. in Computing Science at UTS. He has published more than 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 realworld 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.

Trong Dinh Thac Do is currently a Research Associate in the Advanced Analytics Institute (AAI) at the University of Technology Sydney (UTS), where he also obtained his Ph.D. degree. Before joining AAI-UTS, he received his Master of Philosophy degree in Information Technology in a joint program from the Grenoble Institute of Technology (INPG) and Joseph Fourier University (UJF), France. His research interests include machine learning, statistical models and Bayesian nonparametrics. He has been working in applied machine learning applications since he was a CTO and software engineer. He has published several papers in refereed conferences and journals, such as NIPS (new name NeurIPS), AAAI, IJCAI, ICDM, and International Journal of Knowledge and Information Systems (KAIS). He has served the community as program committee member or reviewer of IJCAI 2019, IJCAI 2018, AAAI 2019, ICDM 2018, ICDM 2017, PAKDD 2019, PAKDD 2018, and reviewer of IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Intelligent Systems, International Journal of Knowledge and Information Systems (KAIS), and International Journal of Data Science and Analytics (JDSA).

Chengzhang Zhu is currently a Ph.D. student in the Advanced Analytics Institute (AAI) at the University of Technology Sydney (UTS). His research interests include metric learning, non-IID learning, and data representation, in addition to general interest in data science especially machine learning. He has published several papers in refereed conferences and journals, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), IJCAI, and ACM Multimedia Conference on Multimedia Conference (MM). He has served the community as program committee member of AAAI 2019, AAAI 2018, AAAI 2017, IJCAI 2019, IJCAI 2018, IJCAI 2017, CIKM 2019, CIKM 2018, PAKDD 2019, PAKDD 2018, PAKDD 2017, PAKDD 2016, and reviewer of IEEE Transactions on Systems, Man and Cybernetics: System, Information Science, IEEE Intelligent Systems, IEEE Access, and International Journal of Data Science and Analytics (JDSA).

Contents

The tutorial includes the following contents:

• The main challenges in complex data and behaviors: a brief introduction of complex data and behaviors and the essential theoretical challenges in learning complex data and behaviors
• What is IID learning: summarize the classic IID learning theories by several examples such as clustering and recommender system theories
• Limitations of IID learning: summarize the main limitations of the classic IID learning methods, and introduce why they do not work well in handling complex data and behaviors
• Related work of non-IID learning: introduce the related work of non-IID learning to address the gaps and issues in classic IID and non-IID data learning theories and models
• Non-IID characteristics and nature: discuss the characteristics of non-IID data, especially the coupling relationships and heterogeneity in complex data and behaviors
• Abstract non-IID learning model and representation: discuss an abstract representation model of non-IIDness by involving the heterogeneity and couplings between entities, properties, contexts and interactions etc.
• Non-IID clustering: new similarity metrics, algorithms and case studies will be presented to introduce non- IIDness into clustering
• Non-IID ensemble clustering: new similarity metrics, algorithms and case studies will be presented to cater for non-IIDness in ensemble clustering
• Non-IID outlier detection: new methods will be introduced to incorporate non-IIDness into outlier detection and outlying feature selection
• Non-IID classification: similarity metrics and algorithms will be introduced to incorporate non-IIDness into classification
• Non-IID recommender systems: non-IID recommender systems including the framework, algorithms and experiments will be introduced
• Term coupling-based document analysis: similarity metrics, algorithms and case study of incorporating non-IID learning into document analysis will be discussed
• Hierarchical structure and couplings in non-IID learning: We summarize the case studies and conclude the hierarchical structure and couplings in non-IID learning as a foundation for non-IID learning
• Challenges and prospects of non-IID information processing and management: open issues and opportunities will be discussed for incorporating non-IIDness into information processing and management, including database management, knowledge management, information retrieval, recommender system, data mining and machine learning to develop corresponding methodologies, systems and algorithms for real-world big data applications.

Related Tutorials

Interested readers can find the following tutorials from their corresponding conferences and here.

• Trong Dinh Thac Do and Longbing Cao. Statistical Machine Learning of Large, Sparse and Multi-source Data, PAKDD 2019.
• Longbing Cao. Behavior analytics: methods and applications, AAAI 2019.
• Longbing Cao, Philip Yu, Guansong Pang. Behavior analytics: methods and applications, KDD 2018.
• Liang Hu, Longbing Cao, Songlei Jian. Non-IID Recommender Systems in Practice with Modern AI Techniques, PAKDD 2018.
• Liang Hu, Longbing Cao, Jian Cao, Songlei Jian. When Advanced Machine Learning Meets Intelligent Recommender Systems, AAAI 2018.
• Longbing Cao, Philip Yu, Guansong Pang and Chengzhang Zhu. Non-IID Learning, KDD 2017.
• Longbing Cao, Behavior Computing: Deep Behavior Analytics and Active Behavior Management, PAKDD 2015.
• Longbing Cao. Learning Non-IID Big Data, CIKM 2014.
• Longbing Cao, Non-IIDness Learning in Big Data, PAKDD 2014.
• Longbing Cao, Philip S Yu, Can Wang, Behavior Informatics: Modeling, Analysis and Mining of Complex Behaviors, 4 Aug 2013, IJCAI 2013, Beijing, China.
• Longbing Cao, Philip S Yu, Can Wang, Behavior Computing: Complex Behavior Modeling, Analysis and Mining, WI-IAT 2012, 4 Dec 2012, Macau, China.
• Longbing Cao, Domain-Driven Data Mining: Empowering Actionable Knowledge Delivery, PAKDD2009.
• Longbing Cao, Agent-mining interaction and integration, IEEE/WIC/ACM WI-IAT2007, USA.

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