RelDenClu:A Relative Density based Biclustering Method for identifying non-linear feature relations with an Application to identify factors effecting spread of COVID-19.

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 data. It has been used for unsupervised learning using three real-life datasets. Applicability of the algorithm is also explored on three real-life datasets to see its utility as an aid to supervised learning. For all the cases the performance of the proposed method is compared with that of seven different state-of-the-art algorithms. The proposed algorithm produces better results. The efficacy of the algorithm is shown on COVID-19 dataset and found out that some features (genetic, demographics and others) are likely to affect the spread of COVID-19.