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Qin, X., Ting, K.M., Zhu, Y. and Lee, V.C. (2019) Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 4755-4762.
https://doi.org/10.1609/aaai.v33i01.33014755

has been cited by the following article:

• TITLE: Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data

  AUTHORS: Tallal Omar, Abdullah Alzahrani, Mohamed Zohdy

  KEYWORDS: K-Means Technique, Elbow Technique, Clustering Technique, Data Mining, Academic Performance

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.8 No.3, August 18, 2020

  ABSTRACT: The academic community is currently confronting some challenges in terms of analyzing and evaluating the progress of a student’s academic performance. In the real world, classifying the performance of the students is a scientifically challenging task. Recently, some studies apply cluster analysis for evaluating the students’ results and utilize statistical techniques to part their score in regard to student’s performance. This approach, however, is not efficient. In this study, we combine two techniques, namely, k-mean and elbow clustering algorithm to evaluate the student’s performance. Based on this combination, the results of performance will be more accurate in analyzing and evaluating the progress of the student’s performance. In this study, the methodology has been implemented to define the diverse fascinating model taking the student test scores.