Data Mining: Introduction, Techniques, and Applications
Data mining is the science of extracting meaningful and potentially useful knowledge from (large) datasets. It has emerged as a young inter-disciplinary field in computer science in the past decade. A wide range of data mining techniques have been developed and applied to applications in industry, science, engineering, and government. Many believe that data mining will have a long-lasting and profound impact on our society.
In this talk, I will introduce you to this new field from several aspects. First, I will identify the driving forces behind this new field. I will also address basic questions such as "What is data mining?", "What is a general data mining process like?", and "Who are doing data mining?". Second, I will identify a list of main data mining techniques and explain each of them using real-world applications. Finally, I will discuss the main challenges one may encounter in the mining process.
At the end of the talk, I will briefly go over several of my on-going research projects at San Francisco State University, in which you may play an active role in the future.
Hui Yang is currently an Assistant Professor in the Department of Computer Science at San Francisco State University. She is also affiliated with the Center for Computing for Life Sciences at SFSU. She received her Ph.D. and M.Sc. degrees in Computer Science from The Ohio State University in 2002 and 2006 respectively. Her primary research area is in the data mining field and its applications, with a current focus on mining spatial and temporal data in bioinformatics.