A new methodology for web-knowledge-based system using systematic thinking, KM process and data & knowledge engineering technology:FBR-GAs-CBRC5.0-CART
In Knowledge Management perspective, Organization Learning and the selection of Knowledge Management tool affects the Knowledge Management strategy planning. Among the various KM theorem such as Learning method, organization knowledge creation, Cognitive theory, Intangible assets and knowledge capit...
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| Format: | Journal |
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2018
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| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84892583062&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52501 |
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| Institution: | Chiang Mai University |
| Summary: | In Knowledge Management perspective, Organization Learning and the selection of Knowledge Management tool affects the Knowledge Management strategy planning. Among the various KM theorem such as Learning method, organization knowledge creation, Cognitive theory, Intangible assets and knowledge capital, Measuring knowledge theory etc., Systematic Thinking plays an important role in Knowledge Management activities especially, the creation of Knowledge Management strategy, KM process and Knowledge Management system. DKET is one of several approaches for implementing the Knowledge Management tools based on the KM strategies. They are not only implemented in forms of standalone system but the web-online system also. Generally, DKET namely Ensemble Learning is well known as the technique of using different training data sets or learning algorithms. Currently, a popular learning algorithm is Fuzzy-Based Reasoning (FBR) which the concept of this theory is "each item is not matched to a given cluster but it has a degree of belonging to a certain cluster". According to these reasons, in this paper, a new methodology for Web-Knowledge-Based System by using Systematic Thinking, Knowledge Process and DKET (FBR-GAs-C5.0-CART) is proposed in terms of KM perspective. The algorithm performance comparisons of Fuzzy C-Means-CBR-GAs-C5.0-CART in several data sets are presented. The satisfied clustering results of Fuzzy-C Means-GAs-CBR-C5.0-CART attain RMSE at 5.10 for the case that full data set, on the other hand the best result of using Fuzzy-C Means-CBR-C5.0-CART attain RMSE at 12.03 in the case that unrecoded variables and CBR-C5.0- CART without symptoms variables. In the future, the other KM theories and DKET will be applied to improve the performance of this system. |
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