Document Clustering Games in Static and Dynamic Scenarios

TL;DR

This paper introduces a game theoretic approach to document clustering, modeling documents as players and clusters as strategies, which adapts well to static and streaming data scenarios.

Contribution

The work presents a novel game theoretic model for document clustering that handles both static and dynamic data, incorporating data geometry and streaming capabilities.

Findings

Performs well on 13 datasets compared to existing algorithms

Effectively clusters streaming and static data

Uses game dynamics to improve clustering accuracy

Abstract

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.