Multi-relational data mining: an introduction

By Saso Dzeroski. SKDD, 2003

296 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e8601c7e1a7fc9825e225411a3c97d41710b95c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Yi Peng
2. Zhengxin Chen
3. Gang Kou
4. Yong Shi

In Information Systems

1. Yi Peng
2. Zhengxin Chen
3. Gang Kou
4. Yong Shi

In Molecular Biology

1. Yi Peng
2. Zhengxin Chen
3. Gang Kou
4. Yong Shi

Citations in the corpus

• A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) (2016, background): “Concept discovery (Muggleton and De Raedt, 1994), one of themost commonly addressed tasks of multi-relational data mining (Dzeroski, 2003), is concerned with inducing the definitions of a relation,(2), called target relation L, in terms of other relations, called background knowledge B, given in the”
• A Descriptive Framework for the Field of Data Mining and Knowledge Discovery (2008, methodology): “MRDM searches for knowledge and patterns from multiple tables in a relational database [Džeroski 2003] and can be expanded to many data mining tasks, such as MR association analysis, MR classification, MR clustering, MR regression, and MR linkage analysis.”