ITEM METADATA RECORD
Title: Ranked tiling
Authors: Le Van, Thanh ×
van Leeuwen, Matthijs
Nijssen, Siegfried
Fierro GutiƩrrez, Ana Carolina Elisa
Marchal, Kathleen
De Raedt, Luc #
Issue Date: 2014
Publisher: Springer
Host Document: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2014 pages:98-113
Conference: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) edition:ECML/PKDD 2014 location:Nancy, France date:15-19 September 2014
Abstract: Tiling is a well-known pattern mining technique. Traditionally, it discovers large areas of ones in binary databases or matrices, where an area is defined by a set of rows and a set of columns. In this paper, we introduce the novel problem of ranked tiling, which is concerned with finding interesting areas in ranked data. In this data, each transaction defines a complete ranking of the columns. Ranked data occurs naturally in applications like sports or other competitions. It is also a useful abstraction when dealing with numeric data in which the rows are incomparable.

We introduce a scoring function for ranked tiling, as well as an algorithm using constraint programming and optimization principles. We empirically evaluate the approach on both synthetic and real-life datasets, and demonstrate the applicability of the framework in several case studies. One case study involves a heterogeneous dataset concerning the discovery of biomarkers for different subtypes of breast cancer patients. An analysis of the tiles by a domain expert shows that our approach can lead to the discovery of novel insights.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Centre of Microbial and Plant Genetics
× corresponding author
# (joint) last author

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