HBIC: A Biclustering Algorithm for Heterogeneous Datasets

TL;DR

HBIC is a novel biclustering algorithm designed for heterogeneous datasets with mixed data types, enabling the discovery of meaningful biclusters in complex real-world data.

Contribution

The paper introduces HBIC, a biclustering method capable of handling numeric, binary, and categorical data, with a two-stage process for bicluster generation and selection.

Findings

Successfully identified high-quality biclusters in synthetic benchmarks.

Demonstrated effectiveness on biomedical clinical data.

Outperformed existing biclustering approaches on heterogeneous datasets.

Abstract

Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. Several biclustering algorithms have been proposed for handling numeric datasets. However, real-world data mining problems often involve heterogeneous datasets with mixed attributes. To address this challenge, we introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data, including numeric, binary, and categorical data. The approach comprises two stages: bicluster generation and bicluster model selection. In the initial stage, several candidate biclusters are generated iteratively by adding and removing rows and columns based on the frequency of values in the original matrix. In the second stage, we introduce two approaches for selecting the most suitable biclusters by considering their size and homogeneity. Through a series of experiments, we investigated the suitability of our approach on a synthetic benchmark and in a biomedical application involving clinical data of systemic sclerosis patients. The evaluation comparing our method to existing approaches demonstrates its ability to discover high-quality biclusters from heterogeneous data. Our biclustering approach is a starting point for heterogeneous bicluster discovery, leading to a better understanding of complex underlying data structures.