Domain-oriented Data-driven Data Mining and Granular Cognitive Computing (3DM&GCC)

Short Description: There are two related parts in this tutorial talk, that is, domain-oriented data-driven data mining (3DM) and granular cognitive computing (GCC). In the 3DM part, a new data mining model of domain-oriented data-driven data mining is introduced. Data is taken as a format for encoding information and knowledge, like brain and book. Data mining is a process of transforming the information and knowledge from data format (which is unstructured, and not understandable for human) into a structured format understandable for human. The characteristics of the knowledge in data should remain unchanged in a data mining process. Some knowledge characteristics, e.g. uncertainty degree, are studied and extracted (measured) from data at first. These knowledge characteristics are used to control the data mining process and thus not changed in the process. This is a data driven data mining process. In a domain oriented data mining process, users are allowed to suggest preferred classifiers and structures; an interactive manner between users and machines is used; its input and output are interleaved, like a conversation; users can freely explore the dataset according to their preference and priority, ensure that each classification stage and the corresponding results are all understandable and comprehensible. Some case studies of 3DM are introduced and future studies of 3DM are discussed.
In the GCC part, inspired by human’s granularity thinking based problem solving mechanism and the cognition law of global precedence”, a data-driven granular cognitive computing model (DGCC) is further introduced. It integrates two contradictory mechanisms, that is, human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”. According to DGCC, deep learning is taken as a combination of symbolism and connectionism, and named hierarchical structuralism (HS). Some characteristics of HS and key research problems about DGCC are addressed.


Guoyin Wang Dr. Guoyin Wang received the bachelor’s degree in computer software, the master’s degree in computer software, and the Ph.D. degree in computer organization and architecture from Xi’an Jiaotong University, Xi’an, China, in 1992, 1994, and 1996, respectively. Since 1996, he has been working at the Chongqing University of Posts and Telecommunications, where he is currently a professor and PhD supervisor, the Director of the Chongqing Key Laboratory of Computational Intelligence. He was named as a national excellent teacher and a national excellent university key teacher of China, in 2001 and 2002 respectively. He was elected as a talent of the Program for New Century Excellent Talents in University of China, in 2004, a National Level Talent of the New Century Hundred, Thousand and Ten Thousand Talents Project of China in 2009, a State Council Expert for Special Allowance in 2010, He was elected as a Science and Technology Innovation Talent of the National High-level Personnel of Special Support Program of China, and a Leading Expert of Chongqing Chief Expert Studio, in 2014. He was elected as a Chang Jiang Scholar by the Ministry of Educations, P. R. China, in 2014. The teaching group directed by Professor Wang was elected as a National Excellent Teaching Group of China in 2010. The institute (ICST) directed by Professor Wang was elected as one of the Top Ten Outstanding Youth Organizations of Chongqing, China, in 2002. The research team directed by Professor Wang was elected as an Innovation Team of Chongqing, China, in 2010. He is a Fellow and the Steering Committee Chair of International Rough Set Society (IRSS), a Vice-President of the Chinese Association for Artificial Intelligence (CAAI), a council member of the China Computer Federation (CCF), and a senior member of IEEE. He had served as the President of IRSS 2014-2016. He served or is currently serving on the program committees of many international conferences and workshops, as program committee member, program chair or co-chair. He is an editorial board member of several journals. The research interests of Professor Wang include big data, data mining, machine learning, rough set, granular computing, knowledge technology, soft computing, cognitive computing, etc. He is the author of over 10 books, the editor of dozens of proceedings of international and national conferences, and has over 200 reviewed research publications.