Abstract
Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, being applied in many scientific scenarios (e.g. bioinformatics, text analysis, computer vision). This paper proposes a novel biclustering approach, which extends the dominant-set clustering algorithm to the biclustering case. In particular, we propose a new way of representing the problem, encoded as a graph, which allows to exploit dominant set to analyse both rows and columns simultaneously. The proposed approach has been tested by using a well known synthetic microarray benchmark, with encouraging results.
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- 1.
The idea is to setup a symmetric, non-cooperative game, called clustering game, between two players. Data points V are the strategies available to the players and the similarity matrix A encodes their payoff matrix.
- 2.
We performed a t-test for each noise level (on the result of the 30 matrices), we set the significance level to 5%.
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Denitto, M., Bicego, M., Farinelli, A., Pelillo, M. (2018). Dominant Set Biclustering. In: Pelillo, M., Hancock, E. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science(), vol 10746. Springer, Cham. https://doi.org/10.1007/978-3-319-78199-0_4
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