  Topic detection is a process for determining topics from a collection of
textual data. One of the topic detection methods is a clustering-based method,
which assumes that the centroids are topics. The clustering method has the
advantage that it can process data with negative representations. Therefore,
the clustering method allows a combination with a broader representation
learning method. In this paper, we adopt deep learning for topic detection by
using a deep autoencoder and fuzzy c-means called deep autoencoder-based fuzzy
c-means (DFCM). The encoder of the autoencoder performs a lower-dimensional
representation learning. Fuzzy c-means groups the lower-dimensional
representation to identify the centroids. The autoencoder's decoder transforms
back the centroids into the original representation to be interpreted as the
topics. Our simulation shows that DFCM improves the coherence score of
eigenspace-based fuzzy c-means (EFCM) and is comparable to the leading standard
methods, i.e., nonnegative matrix factorization (NMF) or latent Dirichlet
allocation (LDA).
