每年專案
摘要
Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform incremental learning challenge its widespread application. To overcome these limitations, this paper introduces a novel approach called parametric spectral clustering (PSC). By extending the capabilities of spectral clustering, PSC addresses the challenges associated with big data and real-time scenarios and enables efficient incremental clustering with new data points. Experimental evaluations conducted on various open datasets demonstrate the superiority of PSC in terms of computational efficiency while achieving clustering quality mostly comparable to standard spectral clustering. The proposed approach has significant potential for incremental and real-time data analysis applications, facilitating timely and accurate clustering in dynamic and evolving datasets. The findings of this research contribute to the advancement of clustering techniques and open new avenues for efficient and effective data analysis. We publish the experimental code at https://github.com/109502518/PSC_BigData.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 |
編輯 | Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal |
發行者 | Institute of Electrical and Electronics Engineers Inc. |
頁面 | 1070-1075 |
頁數 | 6 |
ISBN(電子) | 9798350324457 |
DOIs | |
出版狀態 | 已出版 - 2023 |
事件 | 2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy 持續時間: 15 12月 2023 → 18 12月 2023 |
出版系列
名字 | Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 |
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???event.eventtypes.event.conference??? | 2023 IEEE International Conference on Big Data, BigData 2023 |
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國家/地區 | Italy |
城市 | Sorrento |
期間 | 15/12/23 → 18/12/23 |
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