@inproceedings{a5c5fc073c8445309f24a751956f94eb,
title = "Kernelization of eigenspace-based fuzzy C-Means for topic detection on Indonesian news",
abstract = "Topic detection is practical methods to find a topic in a collection of documents. One of the methods is a clustering-based method whose centroids are interpreted as topics, i.e., eigenspace-based fuzzy c-means (EFCM). The clustering process of the EFCM method is performed in a smaller dimensional Eigenspace. Thus, the accuracy of the clustering process may be reduced. In this paper, we use the kernel method so that the clustering process is performed in a higher dimensional space without transforming data into that space. Our simulations show that this kernelization improves the accuracies of EFCM in term of interpretability scores for Indonesian news.",
keywords = "Clustering, Eigenspace, Fuzzy c-means, Kernel, Topic detection",
author = "Mukti Ari and Hendri Murfi",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th International Conference on Information and Communication Technology, ICoICT 2018 ; Conference date: 03-05-2018 Through 04-05-2018",
year = "2018",
month = nov,
day = "8",
doi = "10.1109/ICoICT.2018.8528786",
language = "English",
series = "2018 6th International Conference on Information and Communication Technology, ICoICT 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "520--525",
booktitle = "2018 6th International Conference on Information and Communication Technology, ICoICT 2018",
address = "United States",
}