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 interconnected 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 uncorrelate...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Hongyu Guo, Herna L. Viktor
• Keywords: Data Mining, Data Mining Algorithms, KDD 2006, Relational Data Mining, Relational Mining Process