Research On Domain Driven Knowledge Discovery Method

Data mining is a common knowledge discovery process which taps potential and useful knowledge from massive data.However, traditional data mining is data-driven, emphasizing automation in data mining process, therefore the mining results often include a lot of redundant, even wrong knowledge which can not be directly applied to real world business activities. Data mining method which aims at mining users'interests and actionable knowledge should embrace domain knowledge, especially experts'preferences, experiences, knowledge and wisdom throughout the mining process, transforming data-driven data mining into domain-driven data mining, so as to bridge the gap between academic research and the real-world application. On the other hand, a lot of knowledge stores in human minds, especially those experts with rich theoretical and practical experiences. Therefore, in order to solve complex problems, experts should be regarded as direct mining targets. In this way, experts'knowledge on complex issues can be obtained, and through the combination of two knowledge discovery modes, we can get more comprehensive and accurate knowledge.With management science, computer science and meta-synthesis as the basis, interesting and actionable knowledge gained from both data and experts is regarded as Domain-Driven Knowledge Discovery, the tasks of this dissertation include:1.Based on analyzing the shortcomings of traditional data mining, it studies theories and methods on how to integrate domain knowledge into the whole data mining process, so as to further enrich the content of the domain-driven data mining theories. As to the deficiencies of traditional data mining model (CRISP-DM), this thesis proposes a new Domain-Driven Data Mining model and introduces the methodology of comprehensive integrated system as the guidance of Domain-Driven Data Mining process.2.It also proposes a semantic Apriori algorithm which integrates domain knowledge into the data mining algorithm in order to meet demands of different levels and various mining purposes.Then, the thesis studies how to get consensus of experts during discussion.3.After analyzing the characteristics of expert knowledge, it builds an expert knowledge model, using correspondence analysis to conduct clustering in both expert and experts'views dimensions simultaneously and mapping on two-dimensional plane in order to tap knowledge among experts, as well as knowledge between experts and experts'views.Meanwhile, a weighted bipartite network projection algorithm is applied to calculate similarity of the experts'opinions, so as to describe similarity and independence of experts'opinions in a quantitative way. 4.Finally, this thesis designs and develops a domain knowledge-driven knowledge discovery platform, and applies the platform to tap academic thoughts of Chinese veteran practioners of TCM from data as well as from experts.The empirical result shows feasibility and advantages of domain-driven knowledge discovery.