Abstract
Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labelled data is available. Indeed, labelled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labelled data. This paper introduces a semi-supervised variant of the Fuzzy ART clustering algorithm (SSFuzzyART). The proposed solution uses the available labelled data to initialize clusters centers. A set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.
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Jendoubi, S., Baelde, A. (2022). SSFuzzyART: A Semi-Supervised Fuzzy ART Through Seeding Initialization. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_58
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DOI: https://doi.org/10.1007/978-3-031-08974-9_58
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