Current trends of granular data mining for biomedical data analysis

Abstract

Biomedical data are available in many different formats, including numeric, textual reports, signals or images, and they come available from a variety of sources. Biomedical data typically suffer from incompleteness, uncertainty and vagueness, posing several challenges to perform data analysis, such high dimensionality, class imbalance or low numbers of samples [ 1 , 2 ]. Granular Computing, the term coined by Prof. L. A. Zadeh, provides a powerful tool for multiple granularity and multiple-view data analysis, which is of vital importance for understanding data driven analysis at different levels of ab- straction (granularity) [3] . It is worth stressing that human’s capabilities in effective information or ganization and efficient reasoning with complex and uncertain information is highly dependent on hierarchical Granular Computing [ 4 , 5 ]. We have been witnessing significant advances of Granular Computing in the scientific and engineering domains. Data mining based on Granular Computing in biomedical data analysis is an emerging field which crosses multiple research disciplines and in- dustry domains. As a meta-mathematical methodology, granular data mining provides a theoretical framework for biomed- ical data analytics. It helps to extract knowledge when we are provided with an insufficient data that may also contain a significant amount of unstructured, uncertain and imprecise data. Granular data mining technology has exhibited some strong capabilities and advantages in intelligent data analysis and uncertainty reasoning for biomedical data. However, de- termining how to integrate Granular Computing and data mining to combine their advantages remains an interesting and important research topic. Recent survey indicated that granular data mining research has been focused on exploring the advantages, and also the challenges, derived from collecting and mining vast amounts of available biomedical data sources. It has therefore become strongly and timely justified to develop theoretical models and practical algorithms for carrying out granular data mining for biomedical data analysis.