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.