In order to optimizing the process of the government agricultural decision-making, based on the theories and technologies of the knowledge management, the meaning of the knowledge-based government is firstly analyzed....In order to optimizing the process of the government agricultural decision-making, based on the theories and technologies of the knowledge management, the meaning of the knowledge-based government is firstly analyzed. Combining with the detailed requirements of government for agricultural knowledge management, an agricultural knowledge management system including the agricultural knowledge sharing system, the agricultural Web data-mining system and the agricultural expert decision system is established in the paper.展开更多
This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clusterin...This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.展开更多
基金Supported by"Dual-support"College-level Special Fund of Sichuan Agriculture University~~
文摘In order to optimizing the process of the government agricultural decision-making, based on the theories and technologies of the knowledge management, the meaning of the knowledge-based government is firstly analyzed. Combining with the detailed requirements of government for agricultural knowledge management, an agricultural knowledge management system including the agricultural knowledge sharing system, the agricultural Web data-mining system and the agricultural expert decision system is established in the paper.
文摘This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory （RST）. The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.
