Advanced search
1 file | 947.56 KB
Author
Organization
Project
Bioinformatics: from nucleotids to networks (N2N)
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.
Keywords
numerical data, ranked data, pattern mining, tiling

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 947.56 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Le Van, Thanh, Matthijs Van Leeuwen, Siegfried Nijssen, Ana Carolina Fierro, Kathleen Marchal, and Luc De Raedt. 2014. “Ranked Tiling.” In Lecture Notes in Artificial Intelligence, ed. Toon Calders, Floriana Esposito, Eyke Hüllermeier, and Rosa Meo, 8725:98–113. Berlin, Germany: Springer.
APA
Le Van, Thanh, Van Leeuwen, M., Nijssen, S., Fierro, A. C., Marchal, K., & De Raedt, L. (2014). Ranked tiling. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Lecture Notes in Artificial Intelligence (Vol. 8725, pp. 98–113). Presented at the 7th European conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2014), Berlin, Germany: Springer.
Vancouver
1.
Le Van T, Van Leeuwen M, Nijssen S, Fierro AC, Marchal K, De Raedt L. Ranked tiling. In: Calders T, Esposito F, Hüllermeier E, Meo R, editors. Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer; 2014. p. 98–113.
MLA
Le Van, Thanh, Matthijs Van Leeuwen, Siegfried Nijssen, et al. “Ranked Tiling.” Lecture Notes in Artificial Intelligence. Ed. Toon Calders et al. Vol. 8725. Berlin, Germany: Springer, 2014. 98–113. Print.
@inproceedings{5945350,
  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.},
  author       = {Le Van, Thanh and Van Leeuwen, Matthijs and Nijssen, Siegfried and Fierro, Ana Carolina and Marchal, Kathleen and De Raedt, Luc},
  booktitle    = {Lecture Notes in Artificial Intelligence},
  editor       = {Calders, Toon and Esposito, Floriana and H{\"u}llermeier, Eyke and Meo, Rosa},
  isbn         = {9783662448519},
  issn         = {0302-9743},
  keyword      = {numerical data,ranked data,pattern mining,tiling},
  language     = {eng},
  location     = {Nancy, France},
  pages        = {98--113},
  publisher    = {Springer},
  title        = {Ranked tiling},
  url          = {http://dx.doi.org/10.1007/978-3-662-44851-9\_7},
  volume       = {8725},
  year         = {2014},
}

Altmetric
View in Altmetric