Advances in processing, mining, and learning complex data: From foundations to real-world applications


Processing, mining, and learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning models from data with a complex structure (e.g., multirelational data, XML data, text data, image data, time series, sequences, graphs, streaming data, and trees) [1–5]. These kinds of data are commonly encountered in many social, economic, scientific, and engineering applications. Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for processing, mining, and learning them. Traditional data analysis methods often require the data to be represented as vectors [6]. However, many data objects in real-world applications, such as chemical compounds in biopharmacy, brain regions in brain health data, users in business networks, and time-series information in medical data, contain rich structure information (e.g., relationships between data and temporal structures). Such a simple feature-vector representation inherently loses the structure information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized. Meanwhile, the data may come from heterogeneous domains [7], such as traditional tabular-based data, sequential patterns, graphs, time-series information, and semistructured data. Novel data analytics methods are desired to discover meaningful knowledge in advanced applications from data objects with complex characteristics. This special issue contributes to the fundamental research in processing, mining, and learning complex data, focusing on the analysis of complex data sources.

Shirui Pan
Shirui Pan
Professor | ARC Future Fellow

My research interests include data mining, machine learning, and graph analysis.