CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2018
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| _version_ | 1866929225351561216 |
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| author | Zhu, Ye Ting, Kai Ming Carman, Mark Angelova, Maia |
| author_facet | Zhu, Ye Ting, Kai Ming Carman, Mark Angelova, Maia |
| contents | The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in the presence of sparse clusters. Many remedies have been suggested; yet, we show that they are partial solutions which do not address the issue satisfactorily. To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset. We show that this can be achieved by using a new multi-dimensional Cumulative Distribution Function in a transform-and-shift method. The method can be applied to every dataset, before the dataset is used in many existing algorithms to match their implicit assumption without algorithmic modification. We show that the proposed method performs better than existing remedies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1810_02897 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities Zhu, Ye Ting, Kai Ming Carman, Mark Angelova, Maia Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in the presence of sparse clusters. Many remedies have been suggested; yet, we show that they are partial solutions which do not address the issue satisfactorily. To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset. We show that this can be achieved by using a new multi-dimensional Cumulative Distribution Function in a transform-and-shift method. The method can be applied to every dataset, before the dataset is used in many existing algorithms to match their implicit assumption without algorithmic modification. We show that the proposed method performs better than existing remedies. |
| title | CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1810.02897 |