Data Mining: Theory of stochastic neural networks, sensitivity analysis and optimization of mining models, discovery science of complex systems, service science, etc.
Recently, data mining has been a subject of substantial interest both in academia and industry. This is due to an increased recognition of significant advantages of data mining (i.e., knowledge discovery) compared to hypothesis-based conventional approaches in diverse domains ranging from business and economics to engineering and sciences. Covering machine learning (AI), statistical analyses, operations research (OR), and database research, the technology of data mining now represents a new indispensable tool to assist in intelligent decision making for the highly complex business environment.
Today, through Internet, companies are facing competition from international sources, communicating electronically with suppliers, and interacting in real-time with customers. The growth of Internet connectivity, the increased availability of data warehouses, and the imperative of ever-swifter responses underscore the urgency of developing new advanced technologies of knowledge discovery in such areas as ERP (Enterprise Resource Planning), SCM (Supply Chain Management) , CRM (Customer Relationship Management), and KM (Knowledge Management) , to build knowledge based economy and society.
In our laboratory, we explore mathematical and information theoretic research on data mining. Our ultimate goal is to establish a generic discovery science for complex business and/or engineering systems. We cover such industrial applications as discovering useful patterns and rules in stock exchanges, and testing business hypotheses hidden in huge volume of data in the market and customer profiles. Data mining technology is vital, we believe, to consolidate the developing explicit knowledge and allow tacit knowledge to be transferred. We add the Service Science as a new discipline of our research to create values and innovation.