Knowledge discovery is generally described as the process of automatic extraction of interesting and previously unknown patterns from large amounts of data. However, intelligent methods that explicitly incorporate semantic information into the discovery process are not yet well established in the literature. The exploitation of context during the mining process is one of the most common techniques to take advantage of the semantics of different application domains. Contextual aspects may intervene in several steps of the discovery process, but the question is how these aspects interact within the process, and the forms that this can take. For instance, in data pre-processing, enriching the data with additional context information can lead to the generation of more meaningful and human understandable patterns, once the generated patterns are directly related to the input data. Also, during the mining task, context may be used as constraints, thus allowing search space reduction, pattern pruning, and the development of more efficient algorithms. Context information and domain knowledge may intervene in the post-processing steps, helping in the explanation of the results. Recent years have witnessed increasing interests in exploiting context information in data mining. Also, data mining has been successfully applied in a number of different fields, such as bioinformatics, business intelligence, finance data analysis, location based services, network data analysis, and social link analysis. This special issue is an example of this, since the selected papers are related to different data mining areas such as privacy, sensor data, text mining, graph mining, and ontologies. As a consequence, context-aware data mining is a never-ending resilience field and attracts growing research efforts from different fields.