Context-based knowledge discovery and its application

Authors:
Lingling Zhang;Xiao Wang;Liang Zhang;Yibing Chen;Yong Shi
Affiliations:
Graduate University of Chinese Academy of Sciences, Beijing, China and Research Centre on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China and Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China and Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China
Venue:
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
Year:
2012

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Abstract

Mankind is inundated by information, but thirst for knowledge. The use of knowledge discovery to identify potentially useful knowledge from massive data has become an important method, which increasingly attracts much attention. In order to solve the problem of too much emphasis on the accuracy of the algorithm while ignoring the context of the application of knowledge existing in traditional knowledge discovery, we proposed theoretical framework of context-based knowledge discovery. Through the study of context representation based on probability distribution and calculation of context variance and distance etc, data selection based on similarity assessment of context is achieved. Further a context-based KNN classification algorithm is designed. Finally the validity of context-based knowledge discovery is verified.