期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：1
期号：6
页码：247-252
出版社：International Journal of Soft Computing & Engineering
摘要：An increasing number of data mining applications involve the analysis of complex and structured types of data and require the use of expressive pattern languages. Many of these applications cannot be solved using traditional data mining algorithms. This observation forms the main motivation for the multi-disciplinary field of Multi-Relational Data Mining (MRDM). Unfortunately, existing “upgrading” approaches, especially those using Logic Programming techniques, often suffer not only from poor scalability when dealing with complex database schemas but also from unsatisfactory predictive performance while handling noisy or numeric values in real-world applications. However, “flattening” strategies tend to require considerable time and effort for the data transformation, result in losing the compact representations of the normalized databases, and produce an extremely large table with huge number of additional attributes and numerous NULL values (missing values). As a result, these difficulties have prevented a wider application of multi relational mining, and post an urgent challenge to the data mining community. To address the above mentioned problems, this article introduces a multiple view approach—where neither “upgrading” nor “flattening” is required— to bridge the gap between propositional learning algorithms and relational databases and current research challenges in the field of Multi relational classification based on Multi View Learning.
关键词：Multi Relational Data Mining; Propositional;Learning; Multi Relational Classification; Relational Learning.
