Incremental Gaussian Mixture Clustering for Data Streams
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910889246982144 |
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| author | Bhanderi, Aniket Bhatnagar, Raj |
| author_facet | Bhanderi, Aniket Bhatnagar, Raj |
| contents | The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defining and updating clusters formed from a streaming dataset. As the clusters are formed we also identify anomalous datapoints that show up far away from all known clusters. With a number of 2-D datasets we demonstrate the effectiveness of discovering the clusters and also identifying anomalous data points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Incremental Gaussian Mixture Clustering for Data Streams Bhanderi, Aniket Bhatnagar, Raj Machine Learning Databases The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defining and updating clusters formed from a streaming dataset. As the clusters are formed we also identify anomalous datapoints that show up far away from all known clusters. With a number of 2-D datasets we demonstrate the effectiveness of discovering the clusters and also identifying anomalous data points. |
| title | Incremental Gaussian Mixture Clustering for Data Streams |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2412.07217 |