Abstract
Clustering deals with the grouping together of data items which are similar amongst themselves and differ to a greater extent in terms of proximity to items of other groups. The problem is that in most institutions, undergraduate thesis titles are not grouped based on similarity and it is time-consuming for research students to search for a thesis report based on similarity or research papers which have similar topics since the titles are just stored sequentially in the database. The low score of Silhouette coefficient using k-means as a clustering algorithm on text clustering motivated to exploit the potentiality of the Enhanced K-Strange points clustering algorithm to obtain better results. The objective of this paper is to group the undergraduate thesis titles using the Enhanced K-Strange points clustering algorithm. The Silhouette coefficient is used to test the cluster quality. The result of the research is a method that can process the titles of the undergraduate thesis and group them into different groups using a clustering technique.
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Acknowledgements
I would like to take this opportunity to express my profound gratitude and deep regard to my Prof. Teslin Jacob, Computer Engineering Department, Goa College of Engineering, for his guidance and valuable feedback and constant encouragement.
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Madeira, M.A., Jacob, T. (2022). The Implementation of Enhanced K-Strange Points Clustering Method in Classifying Undergraduate Thesis Titles. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_21
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DOI: https://doi.org/10.1007/978-981-16-5157-1_21
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