Abstract
One of the automated methods for textual data analysis is topic detection. Fuzzy C-Means is a soft clustering-based method for topic detection. Textual data usually has a high dimensional data, which make Fuzzy C-Means fails for topic detection. An approach to overcome the problem is transforming the textual data into lower dimensional space to identify the memberships of the textual data in clusters and use these memberships to generate topics from the high dimensional textual data in the original space. In this paper, we apply the Fuzzy C-Means in lower dimensional space for topic detection on Indonesian online news. Our simulations show that the Fuzzy C-Means gives comparable accuracies than nonnegative matrix factorization and better accuracies than latent Dirichlet allocation regarding topic interpretation in the form of coherence values.
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Acknowledgment
This work was supported by Universitas Indonesia under PIT 9 2019 grant. Any opinions, findings, and conclusions or recommendations are the authors’ and do not necessarily reflect those of the sponsor.
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Nugraha, P., Rifky Yusdiansyah, M., Murfi, H. (2019). Fuzzy C-Means in Lower Dimensional Space for Topics Detection on Indonesian Online News. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_28
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DOI: https://doi.org/10.1007/978-981-32-9563-6_28
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