Randomspace-Based Fuzzy C-Means for Topic Detection on Indonesia Online News

Yusdiansyah, Muhammad Rifky; Murfi, Hendri; Wibowo, Arie

doi:10.1007/978-3-030-33709-4_12

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In several empirical studies, the combination of FCM with the data transformation approach (Murfi, 2018;P. Nugraha, Rifky Yusdiansyah, & Murfi, 2019) provides better performance than the random projection approach (Yusdiansyah, Murfi, & Wibowo, 2019) for topic detection problems.…”

Section: Related Workmentioning

confidence: 99%

Deep autoencoder-based fuzzy c-means for topic detection

Murfi

Rosaline

Hariadi

2022

Array

Self Cite

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Deep autoencoder-based fuzzy c-means for topic detection

Murfi

Rosaline

Hariadi

2022

Array

Self Cite

View full text Add to dashboard Cite

“…The ability of kernel methods to handle large-scale data is also a concern. In several empirical studies, the combination of FCM with the data transformation approach (Murfi, 2018; Nugraha et al , 2019) provides better performance than the random projection approach (Yusdiansyah et al ., 2019) for topic detection problems.…”

Section: Related Workmentioning

confidence: 99%

A scalable eigenspace-based fuzzy c-means for topic detection

Murfi

2021

DTA

Self Cite

View full text Add to dashboard Cite

PurposeThe aim of this research is to develop an eigenspace-based fuzzy c-means method for scalable topic detection.Design/methodology/approachThe eigenspace-based fuzzy c-means (EFCM) combines representation learning and clustering. The textual data are transformed into a lower-dimensional eigenspace using truncated singular value decomposition. Fuzzy c-means is performed on the eigenspace to identify the centroids of each cluster. The topics are provided by transforming back the centroids into the nonnegative subspace of the original space. In this paper, we extend the EFCM method for scalability by using the two approaches, i.e. single-pass and online. We call the developed topic detection methods as oEFCM and spEFCM.FindingsOur simulation shows that both oEFCM and spEFCM methods provide faster running times than EFCM for data sets that do not fit in memory. However, there is a decrease in the average coherence score. For both data sets that fit and do not fit into memory, the oEFCM method provides a tradeoff between running time and coherence score, which is better than spEFCM.Originality/valueThis research produces a scalable topic detection method. Besides this scalability capability, the developed method also provides a faster running time for the data set that fits in memory.

show abstract