  The text clustering technique is an unsupervised text mining method which are
used to partition a huge amount of text documents into groups. It has been
reported that text clustering algorithms are hard to achieve better performance
than supervised methods and their clustering performance is highly dependent on
the picked text features. Currently, there are many different types of text
feature generation algorithms, each of which extracts text features from some
specific aspects, such as VSM and distributed word embedding, thus seeking a
new way of obtaining features as complete as possible from the corpus is the
key to enhance the clustering effects. In this paper, we present a hybrid
multisource feature fusion (HMFF) framework comprising three components,
feature representation of multimodel, mutual similarity matrices and feature
fusion, in which we construct mutual similarity matrices for each feature
source and fuse discriminative features from mutual similarity matrices by
reducing dimensionality to generate HMFF features, then k-means clustering
algorithm could be configured to partition input samples into groups. The
experimental tests show our HMFF framework outperforms other recently published
algorithms on 7 of 11 public benchmark datasets and has the leading performance
on the rest 4 benchmark datasets as well. At last, we compare HMFF framework
with those competitors on a COVID-19 dataset from the wild with the unknown
cluster count, which shows the clusters generated by HMFF framework partition
those similar samples much closer.
