Relational Data Mining

By Saso Dzeroski. Springer Berlin Heidelberg, 2001

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Machine learning: a review of classification and combining techniques (2006, background): “A significant part of ILP research now goes under the heading of Relational Data Mining and is concerned with finding decision trees from multi-table relational databases ( Dzeroski and Lavrac 2001 ).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, positive): “of nodes, and clustering nodes based on their connectivity patterns. These tasks arise in many settings such as analysis of social networks and biological pathways. For further information on SRL see [1, 2, 3]. In this article, we review a variety of techniques from the SRL community and explain how”
Graph mining: Laws, generators, and algorithms (2006, neutral): “1 Relational learning Relational data mining has attracted a lot of research interest recently [Džeroski and Lavrač 2001]. While traditional data mining looks for patterns in a single database table, relational mining also uses the structure of linkage between multiple tables/relations. For example,”