Optimised Multiple Data Partitions for Cluster-Wise Imputation of Missing Values in Gene Expression Data

It is commonly agreed that the quality of data analysis can be substantially degraded by the presence of missing data. In various domains such as bioinformatics, an effective tool is required for the discovery of useful knowledge from gene expression datasets. One may simply ignore defected samples, while others attempt to either make an algorithm robust to the problem or develop an imputation technique to fill in missing values. This research focuses on the latter and introduces a new hybridisation of cluster- and neighbour-based references to generate an accurate estimate. It also proposes a novel exploitation of multiple clusterings as the source of cluster-wise information, instead of a single data partition that has been studied by existing methods. These data partitions are selected from a pool of base clusterings with respect to both quality and diversity criteria. Another hybridisation is thus established between swarm intelligence and this search problem. In particular, the algorithm of artificial bee colony (ABC) is extensively explored, with two new operators being invented to allow an evolution of solutions, or food sources for bees. Also, two different imputation strategies are provided to generate entries of missing entries in a data matrix, called cluster-only and cluster-neighbour. Based on published gene expression datasets and different experimental settings, the resulting models usually outperform their baselines and recent approaches, which make use of cluster analysis or devise an intelligent determination of nearest neighbours. Furthermore, they have proven competitive to a benchmark technique belonging to the global approach, especially with high missing ratios. Further extensions to iterative refinement and supervised imputation are discussed in addition to parameter analysis.