document: Mining relational data through correlation-based multiple view validation Commercial relational databases currently store vast amounts of real-world data . The data within these relational repositories are represented by multiple relations , which are inter-connected by means of foreign key joins . The mining of such interrelated data poses a major challenge to the data mining community . Unfortunately , traditional data mining algorithms usually only explore one relation , the so-called target relation , thus excluding crucial knowledge embedded in the related so-called background relations . In this paper , we propose a novel approach for classifying relational such domains . This strategy employs multiple views to capture crucial information not only from the target relation , but also from related relations . This information is integrated into the relational mining process . The framework presented here , firstly , explore the relational domain to partition its features space into multiple subsets . Subsequently , these subsets are used to construct multiple uncorrelated views , based on a novel correlation-based view validation method , against the target concept . Finally , the knowledge possessed by multiple views are incorporated into a meta-learning mechanism to augment one another . Based on this framework , a wide range of conventional data mining methods can be applied to mine relational databases . Our experiments on benchmark real-world data sets show that the proposed method achieves promising results both in terms of overall accuracy obtained and run time , when compared with two other relational data mining approaches .
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