TITLE: Advances in algorithms to find interesting and interpretable patterns in data ABSTRACT:
Nowadays, a large amount of data is generated daily by
individuals, businesses and other organizations.
For instance, an industrial system can produce data
such as usage logs, alarm logs, fault logs, sensor data,
and receive data from other systems. Due to the large volumes of data, managing data and analyzing it has become a difficult but important task. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in symbolic data such as shopping data or data generated by industrial systems. The talk will first briefly review early study on designing algorithms for identifying frequent patterns (e.g. itemsets, episodes, rules) in data. Such patterns can be used for instance to identify frequent alarms or faults in telecommunication networks. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, time-sensitive patterns, and interesting subgraphs. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques.
BIOGRAPHY: Philippe Fournier-Viger(Ph.D) is a Canadian researcher, full professor at the Harbin Institute of Technology (Shenzhen, China). Five years after completing his Ph.D., he came to China and became full professor at the Harbin Institute of Technology (Shenzhen), after obtaining a title of national talent from the National Science Foundation of China. He has published more than 300 research papers related to data mining, intelligent systems and applications, which have received more than 7600 citations (H-Index 44). He is associate editor-in-chief of the Applied Intelligence journal (SCI, Q2) and editor-in-chief of Data Science and Pattern Recognition. He is the founder of the popular SPMF data mining library, offering more than 200 algorithms, cited in more than 1,000 research papers. He recently edited several books for Springer such as “High Utility Pattern Mining: Theory, Algorithms and Applications”. He is co-founder of the UDML and MLiSE series of workshops, held at the ICDM, KDD and PKDD conferences. His interests are data mining, algorithm design, pattern mining, sequence mining, big data, and industrial applications.
Erik Cambria
Associate Professor, School of Computer Science and Engineering,
Nanyang Technological University, Singapore
SenticNet: https://business.sentic.net
Erik Cambria is the Founder of SenticNet, a Singapore-based company offering B2B sentiment analysis services, and an Associate Professor at NTU, where he also holds the appointment of Provost Chair in Computer Science and Engineering. Prior to joining NTU, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore) and earned his PhD through a joint programme between the University of Stirling and MIT Media Lab. His research focuses on the ensemble application of symbolic and subsymbolic AI to natural language processing tasks such as sentiment analysis, dialogue systems, and financial forecasting. Erik is recipient of many awards, e.g., the 2019 IEEE Outstanding Early Career Award, he was listed among the 2018 AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is Associate Editor of several journals, e.g., INFFUS, IEEE CIM, and KBS, Special Content Editor of FGCS, Department Editor of IEEE Intelligent Systems, and is involved in many international conferences as program chair and invited speaker.
ABSTRACT:
Nowadays, a large amount of data is generated daily by individuals, businesses and other organizations. For instance, an industrial system can produce data such as usage logs, alarm logs, fault logs, sensor data, and receive data from other systems. Due to the large volumes of data, managing data and analyzing it has become a difficult but important task. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in symbolic data such as shopping data or data generated by industrial systems. The talk will first briefly review early study on designing algorithms for identifying frequent patterns (e.g. itemsets, episodes, rules) in data. Such patterns can be used for instance to identify frequent alarms or faults in telecommunication networks. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, time-sensitive patterns, and interesting subgraphs. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques.
BIOGRAPHY:
Philippe Fournier-Viger(Ph.D) is a Canadian researcher, full professor at the Harbin Institute of Technology (Shenzhen, China). Five years after completing his Ph.D., he came to China and became full professor at the Harbin Institute of Technology (Shenzhen), after obtaining a title of national talent from the National Science Foundation of China. He has published more than 300 research papers related to data mining, intelligent systems and applications, which have received more than 7600 citations (H-Index 44). He is associate editor-in-chief of the Applied Intelligence journal (SCI, Q2) and editor-in-chief of Data Science and Pattern Recognition. He is the founder of the popular SPMF data mining library, offering more than 200 algorithms, cited in more than 1,000 research papers. He recently edited several books for Springer such as “High Utility Pattern Mining: Theory, Algorithms and Applications”. He is co-founder of the UDML and MLiSE series of workshops, held at the ICDM, KDD and PKDD conferences. His interests are data mining, algorithm design, pattern mining, sequence mining, big data, and industrial applications.
Erik Cambria
Associate Professor, School of Computer Science and Engineering, Nanyang Technological University, Singapore
SenticNet: https://business.sentic.net
Erik Cambria is the Founder of SenticNet, a Singapore-based company offering B2B sentiment analysis services, and an Associate Professor at NTU, where he also holds the appointment of Provost Chair in Computer Science and Engineering. Prior to joining NTU, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore) and earned his PhD through a joint programme between the University of Stirling and MIT Media Lab. His research focuses on the ensemble application of symbolic and subsymbolic AI to natural language processing tasks such as sentiment analysis, dialogue systems, and financial forecasting. Erik is recipient of many awards, e.g., the 2019 IEEE Outstanding Early Career Award, he was listed among the 2018 AI's 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is Associate Editor of several journals, e.g., INFFUS, IEEE CIM, and KBS, Special Content Editor of FGCS, Department Editor of IEEE Intelligent Systems, and is involved in many international conferences as program chair and invited speaker.