2016 6th International Conference - Cloud System and Big Data Engineering (Confluence) 2016
DOI: 10.1109/confluence.2016.7508131
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Cited by 28 publications
(18 citation statements)
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“…They then used a silhouette measure (a measurement of the similarity between objects in the same cluster) to select highest suitability that resulted as three clusters. 16 The results showed the number of students distributed through the three clusters, which could help university management obtain a better idea about student performance. Although this study was simple and concise, it could be improved by applying other data mining algorithms or by increasing the data set to include a broader number of students and/or a larger number of attributes.…”
Section: Clusteringmentioning
confidence: 98%
See 1 more Smart Citation
“…They then used a silhouette measure (a measurement of the similarity between objects in the same cluster) to select highest suitability that resulted as three clusters. 16 The results showed the number of students distributed through the three clusters, which could help university management obtain a better idea about student performance. Although this study was simple and concise, it could be improved by applying other data mining algorithms or by increasing the data set to include a broader number of students and/or a larger number of attributes.…”
Section: Clusteringmentioning
confidence: 98%
“…Singh et al 16 applied the K-means clustering algorithm in research that aimed to understand and enhance student performance at the university level. The authors used a data set of 99 complete student records and applied K-means with different numbers of clusters each time.…”
Section: Clusteringmentioning
confidence: 99%
“…Cerezo et al [4] Singh et al [17] Najdi et al [18] Li et al [19] Fan et al [20] Yamasari et al [21] Yamasari et al [23] Shankar et al [31] Rosa et al [33] Campagni et al [34] the categorybased feature extraction Yes, the random selection The outcomes of the clustering depend on the group members ' study The clustering result depends on the group members ' study Clustering of students' interaction patterns on LMS using EM and K-means Clustering of students' academic performance using K-means Clustering of students' typologies using Kmeans…”
Section: Descriptionmentioning
confidence: 99%
“…The other research also evaluates its performance by silhouette [23]. Next, using the silhouette index as unsupervised evaluation also is applied by research [17]. On the other side, the research [20] evaluates the clustering result using supervised evaluation, namely: accuracy and time.…”
Section: Clustering Evaluation In Educational Data Miningmentioning
confidence: 99%
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