Hierarchical clustering with deep Q-learning

Richard Forster; Agnes Fulop

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arXiv · 1805.10900

Hierarchical clustering with deep Q-learning

Richard Forster·Agnes Fulop

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Abstract

The reconstruction and analyzation of high energy particle physics data is just as important as the analyzation of the structure in real world networks. In a previous study it was explored how hierarchical clustering algorithms can be combined with kt cluster algorithms to provide a more generic clusterization method. Building on that, this paper explores the possibilities to involve deep learning in the process of cluster computation, by applying reinforcement learning techniques. The result is a model, that by learning on a modest dataset of 10; 000 nodes during 70 epochs can reach 83; 77% precision in predicting the appropriate clusters.

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Richard Forster, Agnes Fulop. 2018-05-28. Hierarchical clustering with deep Q-learning. https://arxiv.org/abs/1805.10900

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