Validation of Mixed-structured Data Using Pattern Mining and Information Extraction

For large-scale data mining utilizing data from ubiquitous and mixed-structured data sources, the appropriate extraction and integration into a comprehensive data-warehouse is of prime importance. Then, appropriate methods for validation and potential refinement are essential. This paper presents an approach applying data mining and information extraction methods for data validation: We apply subgroup discovery and (rule-based) information extraction for data integration and validation. The methods are integrated into an incremental process for continuous validation options. The results of a medical application demonstrate that subgroup discovery and the applied information extraction methods are well suited for mining, extracting and validating clinically relevant knowledge.