Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916100577427456 |
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| author | Aryal, Sunil Wells, Jonathan R. Baniya, Arbind Agrahari Santosh, KC |
| author_facet | Aryal, Sunil Wells, Jonathan R. Baniya, Arbind Agrahari Santosh, KC |
| contents | In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing Aryal, Sunil Wells, Jonathan R. Baniya, Arbind Agrahari Santosh, KC Machine Learning In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithms-namely KMeans, DBSCAN, and DP (Density Peak)-across a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results. |
| title | Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.11402 |