Deterministic Initialization of k-means Clustering by Data Distribution Guide

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dc.contributor.authorSirikayon, Chaloemphon
dc.contributor.authorThammano, Arit
dc.date.accessioned2026-08-06T10:35:52Z
dc.date.available2026-08-06T10:35:52Z
dc.date.issued2022-01-01
dc.description.abstractClustering by the k-means is the most widely used method because of its ease of use. But the disadvantage of the k-means algorithm is that it relies on a random initialization. Therefore, the results obtained from each clustering are not stable depending on the starting point, affecting the results obtained in other applications. This paper, therefore, presents a method for determining the initialization of the k-means algorithm using the Data Distribution Guide (DDG). And use it as an aid in determining the starting point without random. Make the results of clustering always equal. And from the experimental results, We found that the accuracy obtained from clustering using the initialization from this method was good. Compared to the commonly used initialization designation.
dc.identifier.citation7th International Conference on Digital Arts Media and Technology Damt 2022 and 5th Ecti Northern Section Conference on Electrical Electronics Computer and Telecommunications Engineering Ncon 2022, 279-284, 2022
dc.identifier.doi10.1109/ECTIDAMTNCON53731.2022.9720377
dc.identifier.other2-s2.0-85127613296
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12913
dc.source7th International Conference on Digital Arts Media and Technology Damt 2022 and 5th Ecti Northern Section Conference on Electrical Electronics Computer and Telecommunications Engineering Ncon 2022
dc.subjectClassification
dc.subjectClustering
dc.subjectDeterministic k-means
dc.subjectk-means Initialization
dc.titleDeterministic Initialization of k-means Clustering by Data Distribution Guide
dc.typeConference Paper

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