Document Clustering Games

Rocco Tripodi, Marcello Pelillo

2016

Abstract

In this article we propose a new model for document clustering, based on game theoretic principles. Each document to be clustered is represented as a player, in the game theoretic sense, and each cluster as a strategy that the players have to choose in order to maximize their payoff. The geometry of the data is modeled as a graph, which encodes the pairwise similarity among each document and the games are played among similar players. In each game the players update their strategies, according to what strategy has been effective in previous games. The Dominant Set clustering algorithm is used to find the prototypical elements of each cluster. This information is used in order to divide the players in two disjoint sets, one collecting labeled players, which always play a definite strategy and the other one collecting unlabeled players, which update their strategy at each iteration of the games. The evaluation of the system was conducted on 13 document datasets and shows that the proposed method performs well compared to different document clustering algorithms.

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Paper Citation


in Harvard Style

Tripodi R. and Pelillo M. (2016). Document Clustering Games . In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 109-118. DOI: 10.5220/0005798601090118


in Bibtex Style

@conference{icpram16,
author={Rocco Tripodi and Marcello Pelillo},
title={Document Clustering Games},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={109-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005798601090118},
isbn={978-989-758-173-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Document Clustering Games
SN - 978-989-758-173-1
AU - Tripodi R.
AU - Pelillo M.
PY - 2016
SP - 109
EP - 118
DO - 10.5220/0005798601090118