Journal
MATHEMATICS
Volume 11, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/math11143063
Keywords
unsupervised learning; machine learning; artificial intelligence; uncertainty
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This paper presents the development of a novel algorithm called RUN-ICON for unsupervised learning. It aims to improve the reliability and confidence of unsupervised clustering by leveraging the K-means++ method and introducing novel metrics. The algorithm has notable characteristics such as robustness, high-quality clustering, automation, and flexibility, and extensive testing has demonstrated its capability to determine the optimal number of clusters under different scenarios. It will soon undergo rigorous testing in real-world scenarios to further prove its effectiveness.
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.
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