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http://dx.doi.org/10.15207/JKCS.2019.10.9.011

Knowledge Reasoning Model using Association Rules and Clustering Analysis of Multi-Context  

Shin, Dong-Hoon (Department of Computer Science, Kyonggi University)
Kim, Min-Jeong (Department of Computer Science, Kyonggi University)
Oh, SangYeob (Division of Computer Engineering, Gachon University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.9, 2019 , pp. 11-16 More about this Journal
Abstract
People are subject to time sanctions in a busy modern society. Therefore, people find it difficult to eat simple junk food and even exercise, which is bad for their health. As a result, the incidence of chronic diseases is increasing. Also, the importance of making accurate and appropriate inferences to individual characteristics is growing due to unnecessary information overload phenomenon. In this paper, we propose a knowledge reasoning model using association rules and cluster analysis of multi-contexts. The proposed method provides a personalized healthcare to users by generating association rules based on the clusters based on multi-context information. This can reduce the incidence of each disease by inferring the risk for each disease. In addition, the model proposed by the performance assessment shows that the F-measure value is 0.027 higher than the comparison model, and is highly regarded than the comparison model.
Keywords
Multi-Context; Cluster Analysis; Association Rules; Knowledge Reasoning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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