Ranked Tiling
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.
Cite
Text
Le Van et al. "Ranked Tiling." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44851-9_7Markdown
[Le Van et al. "Ranked Tiling." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/van2014ecmlpkdd-ranked/) doi:10.1007/978-3-662-44851-9_7BibTeX
@inproceedings{van2014ecmlpkdd-ranked,
title = {{Ranked Tiling}},
author = {Le Van, Thanh and van Leeuwen, Matthijs and Nijssen, Siegfried and Fierro, Ana Carolina and Marchal, Kathleen and De Raedt, Luc},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {98-113},
doi = {10.1007/978-3-662-44851-9_7},
url = {https://mlanthology.org/ecmlpkdd/2014/van2014ecmlpkdd-ranked/}
}