Document Clustering Games in Static and Dynamic Scenarios

  • Rocco TripodiEmail author
  • Marcello Pelillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10163)


In this work we propose a game theoretic model for document clustering. Each document to be clustered is represented as a player and each cluster as a strategy. The players receive a reward interacting with other players that they try to maximize choosing their best strategies. The geometry of the data is modeled with a weighted graph that encodes the pairwise similarity among documents, so that similar players are constrained to choose similar strategies, updating their strategy preferences at each iteration of the games. We used different approaches to find the prototypical elements of the clusters and with this information we divided the players into two disjoint sets, one collecting players with a definite strategy and the other one collecting players that try to learn from others the correct strategy to play. The latter set of players can be considered as new data points that have to be clustered according to previous information. This representation is useful in scenarios in which the data are streamed continuously. The evaluation of the system was conducted on 13 document datasets using different settings. It shows that the proposed method performs well compared to different document clustering algorithms.



This work was partly supported by the Samsung Global Research Outreach Program.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.ECLTCa’ Foscari University, Ca’ MinichVeniceItaly
  2. 2.DAISCa’ Foscari University, Via TorinoVeniceItaly

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