Affiliations: [a] Graduate University of Chinese Academy of Sciences, Beijing, China | [b] Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China | [c] College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA | [d] School of Information Systems, Computing & Mathematics, Brunel University, Uxbridge, Middlesex, UK
Abstract: Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts' experience. Its unique irreplaceability and complementarity has brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the information era. The challenging problem, however, is whether the results of data mining can be really regarded as “knowledge”. To address this issue, the theory of knowledge management should be applied. Unfortunately, there appears little work in the cross-field between data mining and knowledge management. In data mining, researchers focus on how to explore algorithms to extract patterns that are non-trivial, implicit, previously unknown and potentially useful, but overlook the knowledge components of these patterns. In knowledge management, most scholars investigate methodologies or frameworks of using existing knowledge (either implicit or explicit ones) support business decisions while the detailed technical process of uncovering knowledge from databases is ignored. This paper aims to bridge the gap between these two fields by trying to establish foundations of intelligent knowledge management using large data bases. It enables to generate “special” knowledge, called intelligent knowledge base on the hidden patterns created by data mining. Furthermore, this paper systematically analyzes the process of intelligent knowledge management – a new proposition from original data, rough knowledge, intelligent knowledge, and actionable knowledge as well as the four transformations (4T) of these items. This study not only promotes more significant research beyond data mining, but also enhances the quantitative analysis of knowledge management on hidden patterns from data mining.
