  The existing biclustering algorithms for finding feature relation based
biclusters often depend on assumptions like monotonicity or linearity. Though a
few algorithms overcome this problem by using density-based methods, they tend
to miss out many biclusters because they use global criteria for identifying
dense regions. The proposed method, RelDenClu uses the local variations in
marginal and joint densities for each pair of features to find the subset of
observations, which forms the bases of the relation between them. It then finds
the set of features connected by a common set of observations, resulting in a
bicluster.
  To show the effectiveness of the proposed methodology, experimentation has
been carried out on fifteen types of simulated datasets. Further, it has been
applied to six real-life datasets. For three of these real-life datasets, the
proposed method is used for unsupervised learning, while for other three
real-life datasets it is used as an aid to supervised learning. For all the
datasets the performance of the proposed method is compared with that of seven
different state-of-the-art algorithms and the proposed algorithm is seen to
produce better results. The efficacy of proposed algorithm is also seen by its
use on COVID-19 dataset for identifying some features (genetic, demographics
and others) that are likely to affect the spread of COVID-19.
