RelDenClu: A Relative Density based Biclustering Method for identifying non-linear feature relations
This addresses the limitation of existing biclustering methods that rely on linearity assumptions, offering a more flexible approach for data analysis in domains like genomics and epidemiology.
The paper tackles the problem of identifying non-linear feature relations in biclustering by proposing RelDenClu, which uses local density variations to find biclusters, and it outperforms seven state-of-the-art algorithms on simulated and real-life datasets, including a COVID-19 application.
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.