Reducing Uncertainty and Increasing Confidence in Unsupervised Learning
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/14/3063 |
| _version_ | 1797588445595435008 |
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| author | Nicholas Christakis Dimitris Drikakis |
| author_facet | Nicholas Christakis Dimitris Drikakis |
| author_sort | Nicholas Christakis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:52:06Z |
| format | Article |
| id | doaj.art-c369c84c826e429f858b6188b1c7668a |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T00:52:06Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c369c84c826e429f858b6188b1c7668a2023-11-18T20:19:59ZengMDPI AGMathematics2227-73902023-07-011114306310.3390/math11143063Reducing Uncertainty and Increasing Confidence in Unsupervised LearningNicholas Christakis0Dimitris Drikakis1Institute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, CyprusInstitute for Advanced Modelling and Simulation, University of Nicosia, Nicosia CY-2417, CyprusThis 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.https://www.mdpi.com/2227-7390/11/14/3063unsupervised learningmachine learningartificial intelligenceuncertainty |
| spellingShingle | Nicholas Christakis Dimitris Drikakis Reducing Uncertainty and Increasing Confidence in Unsupervised Learning Mathematics unsupervised learning machine learning artificial intelligence uncertainty |
| title | Reducing Uncertainty and Increasing Confidence in Unsupervised Learning |
| title_full | Reducing Uncertainty and Increasing Confidence in Unsupervised Learning |
| title_fullStr | Reducing Uncertainty and Increasing Confidence in Unsupervised Learning |
| title_full_unstemmed | Reducing Uncertainty and Increasing Confidence in Unsupervised Learning |
| title_short | Reducing Uncertainty and Increasing Confidence in Unsupervised Learning |
| title_sort | reducing uncertainty and increasing confidence in unsupervised learning |
| topic | unsupervised learning machine learning artificial intelligence uncertainty |
| url | https://www.mdpi.com/2227-7390/11/14/3063 |
| work_keys_str_mv | AT nicholaschristakis reducinguncertaintyandincreasingconfidenceinunsupervisedlearning AT dimitrisdrikakis reducinguncertaintyandincreasingconfidenceinunsupervisedlearning |