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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.00851 |
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| _version_ | 1866916605578969088 |
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| author | Herdiana, Indra Kamal, M Alfin Triyani Estri, Mutia Nur Renny |
| author_facet | Herdiana, Indra Kamal, M Alfin Triyani Estri, Mutia Nur Renny |
| contents | K-means clustering is an unsupervised clustering method that requires an initial decision of number of clusters. One method to determine the number of clusters is the elbow method, a heuristic method that relies on visual representation. The method uses the number based on the elbow point, the point closest to 90 degrees that indicates the most optimum number of clusters. This research improves the elbow method such that it becomes an objective method. We use the analytical geometric formula to calculate an angle between lines and real analysis principle of derivative to simplify the elbow point determination. We also consider every possibility of the elbow method graph behaviour such that the algorithm is universally applicable. The result is that the elbow point can be measured precisely with a simple algorithm that does not involve complex functions or calculations. This improved method gives an alternative of more reliable cluster determination method that contributes to more optimum k-means clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A More Precise Elbow Method for Optimum K-means Clustering Herdiana, Indra Kamal, M Alfin Triyani Estri, Mutia Nur Renny Methodology 91C20, 62-08, 62A09 K-means clustering is an unsupervised clustering method that requires an initial decision of number of clusters. One method to determine the number of clusters is the elbow method, a heuristic method that relies on visual representation. The method uses the number based on the elbow point, the point closest to 90 degrees that indicates the most optimum number of clusters. This research improves the elbow method such that it becomes an objective method. We use the analytical geometric formula to calculate an angle between lines and real analysis principle of derivative to simplify the elbow point determination. We also consider every possibility of the elbow method graph behaviour such that the algorithm is universally applicable. The result is that the elbow point can be measured precisely with a simple algorithm that does not involve complex functions or calculations. This improved method gives an alternative of more reliable cluster determination method that contributes to more optimum k-means clustering. |
| title | A More Precise Elbow Method for Optimum K-means Clustering |
| topic | Methodology 91C20, 62-08, 62A09 |
| url | https://arxiv.org/abs/2502.00851 |