Reducing Uncertainty and Increasing Confidence in Unsupervised Learning

Nicholas Christakis () and Dimitris Drikakis ()
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Nicholas Christakis: Institute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, Cyprus
Dimitris Drikakis: Institute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, Cyprus

Mathematics, 2023, vol. 11, issue 14, 1-17

Abstract: This paper presents the development of a novel algorithm for unsupervised learning called RUN-ICON (Reduce UNcertainty and Increase CONfidence). The primary objective of the algorithm is to enhance the reliability and confidence of unsupervised clustering. RUN-ICON leverages the K-means++ method to identify the most frequently occurring dominant centres through multiple repetitions. It distinguishes itself from existing K-means variants by introducing novel metrics, such as the Clustering Dominance Index and Uncertainty, instead of relying solely on the Sum of Squared Errors, for identifying the most dominant clusters. The algorithm exhibits notable characteristics such as robustness, high-quality clustering, automation, and flexibility. Extensive testing on diverse data sets with varying characteristics demonstrates its capability to determine the optimal number of clusters under different scenarios. The algorithm will soon be deployed in real-world scenarios, where it will undergo rigorous testing against data sets based on measurements and simulations, further proving its effectiveness.

Keywords: unsupervised learning; machine learning; artificial intelligence; uncertainty (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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