SIS Faculty Job Talk Seminar by Simon LI Yiu Keung | Knowledge Management and Data Mining methods: Classification, Association, and Clustering

Knowledge Management and Data Mining methods:
Classification, Association, and Clustering

Data mining is one of the important means for facilitating decision making in many business areas. Without proper information derived from the data to generate knowledge, we can hardly make informed decisions. Therefore, we need to understand how data mining can help us. With more and more advanced data mining tools are available, performing data mining become much easier. However, the real problem hidden behind is whether we can really generate useful knowledge from the data to give us insights for making sound decisions. In this seminar, I am going to present the principles of the DIKW framework to show how knowledge can be generated from information. The basics of the data mining methods and their applications are presented as a technical supplement to the DIKW framework. A free data mining tool called Weka would be used during the presentation. 

About the Speaker

Dr Li earned the degrees of Doctor of Engineering and Master of Science in Knowledge Management from the Department of Industrial and System Engineering of the Hong Kong Polytechnic University in 2020 and 2010 respectively. His research interests include knowledge and innovation management, big data analytics, data science, business intelligence and data mining. Dr. Li is serving several Hong Kong-based universities in the areas of teaching and researching on business intelligence, data mining, data science, information systems, and computer science. Prior to joining academia, Dr. Li had spent more than 25 years serving the information technology areas of both private and public sectors of Hong Kong.

He is a lecturer-track faculty candidate.