Purpose Studies have shown the association between tongue color anddiseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of t...
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Purpose Studies have shown the association between tongue color anddiseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2d *** total of 595 typical tongue images were analyzed. The 3d information extracted from the image was transformed into 2d information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI).ResultsThe new 2d information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, *** 2d information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification anddiagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.
Education greatly aids in the process of students' growth;therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discov...
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Education greatly aids in the process of students' growth;therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clustering algorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy C-means clustering algorithm using 2d and 3dclustering to evaluate students' performance based on their examination results (the examination grades from College of Computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2d and 3dclustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.
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