Scalability and efficiency in multi-relational data mining

Multi-Relational Data Mining

An important aspect of data mining algorithms and systems is that they should scale well to large databases. A consequence of this is that most data mining tools are based on machine learning algorithms that work on data in attribute-value format. Experience has proven that such ’single-table’ mining algorithms indeed scale well. The downside of this format is, however, that more complex patter...

Efficient Multi-Relational Data Mining

Multi-relational data mining in Microsoft SQL

Most real life data are relational by nature. Database mining integration is an essential goal to be achieved. Microsoft SQL Server (MSSQL) seems to provide an interesting and promising environment to develop aggregated multi-relational data mining algorithms by using nested tables and the plug-in algorithm approach. However, it is currently unclear how these nested tables can best be used by d...

Neural Networks in Multi-Relational Data Mining

Neural networks are non-parametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Multi-relational data mining framework is based on the search for interesting patterns in the relational database. Multi-relational data mining algorithms search a large hypothesis space in order to find a suitable model for a given data set. Although neural networks ...

Multi-relational Data Mining in Medical Databases

This paper presents the application of a method for mining data in a multi-relational database that contains some information about patients strucked down by chronic hepatitis. Our approach may be used on any kind of multirelational database and aims at extracting probabilistic tree patterns from a database using Grammatical Inference techniques. We propose to use a representation of the databa...