An important task in multi-relational data mining is link-based classification which takes
advantage of attributes of links and linked entities, to predict the class label. The relational …

Classification is one of the most popular data mining tasks with a wide range of applications,
and lots of algorithms have been proposed to build accurate and scalable classifiers. Most of …

Many databases store data in relational format, with different types of entities and
information about their attributes and links between the entities. Link-based classification …

We describe experiments with an implementation of Multi-relational decision tree learning
(MRDTL) algorithm for induction of decision trees from relational databases using an …

Collective classification models exploit the dependencies in a network of objects to improve
predictions. For example, in a network of web pages, the topic of a page may depend on the …

Relational databases are the most popular repository for structured data, and is thus one of
the richest sources of knowledge in the world. In a relational database, multiple relations are …

When predicting class labels for objects within a relational database, it is often helpful to
consider a model for relationships: this allows for information between class labels to be …

Nowadays, the expansion of computer networks and the diversity of data sources require
new data mining approaches in multi-database systems. We propose a classification …

In many supervised learning tasks, the entities to be labeled are related to each other in
complex ways and their labels are not independent. For example, in hypertext classification …

In recent years, there has been growing interest in multi-relational classification research
and application, which addresses the difficulties in dealing with large relation search space …