Abstract
In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection. Conversely to standard clustering algorithms, each agent is in charge of discovering well-formed (compact and populated) clusters and, at the same time, a suitable subset of features corresponding to the subspace where such clusters lie, following a local metric learning approach, where each cluster is characterised by its own subset of relevant features. This not only might lead to a deeper knowledge of the dataset at hand, revealing clusters that are not evident when using the whole set of features, but can also be suitable for large datasets, as each agent processes a small subset of patterns. We show the effectiveness of our algorithm on synthetic datasets, remarking some interesting future work scenarios and extensions.
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Notes
- 1.
In the sense that they must be able to deal with an heterogeneous genetic code such as (3.2).
- 2.
Such value is user-configurable and it is defined as a percentage of the number of survived agents.
- 3.
Such value now acts more like a cluster quality measure rather than a fitness value, as we are evaluating a list of clusters rather than individuals from a genetic population.
- 4.
We omit the penalty factor \(\beta \) (and, by extension, the reward factor \(\alpha \)) as they mainly drive RL-BSAS rather then describe the final clusters and/or agents.
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The Authors would like to thank Daniele Sartori for his help in implementing and testing E-ABC.
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Martino, A., Giampieri, M., Luzi, M., Rizzi, A. (2019). Data Mining by Evolving Agents for Clusters Discovery and Metric Learning. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_3
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