A COMPARATIVE ANALYSIS OF FCM, DBSCAN, AND IFMSC-C3A ALGORITHMS FOR PREDICTING STUDENT’S LEARNING STYLES
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Machine Learning is a growing technology that enables computers to learn from past data. This paper concentrates on matching several data mining algorithms by means of the MATLAB tool and various comparison models. Among many characteristics of the student, the preferred learning style is considered the most dominant one. The dataset was taken from the Department of Information Technology, Dr.N.G.P. Arts and Science College and it consisted of 350 students’ data. Cluster analysis is said to a machine learning technique, that clusters the unlabeled dataset. Three different clustering methods namely FCM, DBSCAN, and IFMSC-C3A are applied to make a model for learning styles prediction and offer the best experience for each learner. Among these techniques, IFMSC-C3A performed well in clustering the student dataset.
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1. Md. Hasibur Rahman; Md. Rabiul Islam, “Predict Student's Academic Performance and Evaluate the Impact of Different Attributes on the Performance Using Data Mining Techniques”, 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), DOI: 10.1109/ICEEE42148.2017, 27-29 Dec. 2017 2. M. Sitha Ram; V. Srija; V. Bhargav; A. Madhavi; G. Sai Kumar, Machine Learning Based Student Academic Performance Prediction, 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 10.1109/ICIRCA51532.2021.9544538, 02-04 September 2021 3. JanmenjoyNayak, BighnarajNaik, D.P.Kanungo, H.S.Behera, “A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering”, Ain Shams Engineering Journal Volume 9, Issue 3, September 2018, Pages 379-393 4. P.Menaka, Dr.K.Nandhini, “ Clustering Approaches used in Educational data mining - A Research Travelogue”, International Journal of Innovations & Advancement in Computer Science, ISSN 2347 – 8616, Volume 6, Issue 7, July 2017 5. P.Menaka, Dr.K.Nandhini, ”Performance of Data Mining Classifiers on Kolb’s Learning Style Inventory (KLSI)”, Indian Journal of Science and Technology, Vol 12(23), DOI:10.17485/ijst/2019/v12i23/145370, June 2019 6. H. Du, S. Chen, H. Niu, and Y. Li, "Application of DBSCAN clustering algorithm in evaluating students' learning status," 2021 17th International Conference on Computational Intelligence and Security (CIS), 2021, pp. 372 -376, doi: 10.1109 /CIS54983. 2021. 00084. 7. Igor de Moura Ventorim, Diego Luchi, Alexandre Loureiros Rodrigues, Flávio Miguel Varejão, “BIRCHSCAN: A sampling method for applying DBSCAN to large datasets”, Expert Systems with Applications, Volume 184, 1 December 2021, 115518 8. Ming-Chuan Hung, Don-Lin Yang, “An efficient Fuzzy C-Means clustering algorithm”, Proceedings 2001 IEEE International Conference on Data Mining, DOI: 10.1109/ICDM.2001.989523, 29 November 2001 - 02 December 2001 9. P. Menaka, Dr.K.Nandhini, “ Prediction of Student performance using Intuitionistic Fuzzy Mean Shift Clustering boosted with Chaotic Cheetah Chase Algorithm”, Turkish Journal of Computer and Mathematics Education, Vol.12 No.10 (2021), 3940-3947. 10. S.Manochandar et.al, “Development of new seed with modified validity measures for k-means clustering”, Computers and Industrial Engineering, Volume 141, March 2020, 106290 11. Qi Li, Shihong Yue, Yaru Wang, Mingliang Ding, And Jia Li, “A New Cluster Validity Index Based on the Adjustment of Within-Cluster Distance”, IEEE Access, n November 5, 2020, Digital Object Identifier 10.1109/ACCESS.2020.3036074
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