Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A vector space model for automatic indexing
Communications of the ACM
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Doing more with more information: Changing healthcare planning with OLAP tools
Decision Support Systems
DARA: Data Summarisation with Feature Construction
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Discretization numbers for multiple-instances problem in relational database
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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This paper helps the understanding and development of a data summarisation approach that summarises structured data stored in a non-target table that has many-to-onerelations with the target table. In this paper, the feasibility of data summarisation techniques, borrowed from the Information Retrieval Theory, to summarise patterns obtained from data stored across multiple tables with one-to-many relations is demonstrated. The paper describes the Dynamic Aggregation of Relational Attributes (DARA) framework, which summarises data stored in non-target tables in order to facilitate data modelling efforts in a multi-relational setting. The application of the DARA algorithm involving structured data is presented in order to show the adaptability of this algorithm to real world problems.