Research on Case Knowledge Evolution Based on Two-dimensional Compression and Comprehensive Activity
As the core resource of enterprise value creation,the life cycle of knowledge accelerates to be shortened,resulting in the problems of knowledge inefficiency,invalidation and redundancy.However,knowledge evolution effectively alleviates the above problems,improves the quality of case base and the application utility of knowledge.Therefore,a case knowledge evolution method based on two-dimensional compression and comprehensive activity is proposed.Firstly,the C4.5-NRS algorithm is used to reduce the case attribute set,and the case base is vertically compressed to reduce the workload of subsequent calculation and the impact of redundant attributes.Secondly,the case space is horizontally compressed based on the improved K-means clustering,and the case clusters to be evolved are delineated by combining the information entropy of the cluster center.Then,the comprehensive activity of the case knowledge to be evolved is calculated based on the indicators of timeliness activity,application activity,scarcity activity and entropy activity.Finally,the evolutionary operation of the case to be evolved is determined according to the established threshold.The simulation results show that the average activity and operation efficiency of the evolved case base are significantly improved.Among them,the double-dimensional spatial compression of the evolution space improves the computing efficiency,while by adding updating and dormancy operations on the basis of the binary operation of retention and deletion,the activity of the cases with medium activity is enhanced to ensure the coordinated development of the inventories and quality of the case base.
knowledge evolutioncase knowledgecase base qualitycomprehensive activityknowledge managementtwo-dimensional space compressionneighborhood rough set
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