Empirical studies on applying density-based clustering to stream data
- Authors
- Kim, Yijin; Park, Jung-Eun; Youn, Jonghem; 심준호
- Issue Date
- Jun-2020
- Publisher
- ICIC International
- Citation
- ICIC Express Letters, Part B: Applications, v.11, no.6, pp 615 - 622
- Pages
- 8
- Journal Title
- ICIC Express Letters, Part B: Applications
- Volume
- 11
- Number
- 6
- Start Page
- 615
- End Page
- 622
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1454
- DOI
- 10.24507/icicelb.11.06.615
- ISSN
- 2185-2766
- Abstract
- Density-based clustering has advantages over partition-based clustering, such as K-means, in that it does not need to specify the number of clusters (k) and can generate clusters of arbitrary shape. However, density-based clustering requires hyper-parameters such as proximity distance and the minimum number of proximity data that are suitable for data characteristics, and this greatly influences the clustering performance. In this paper, we present a density-based clustering algorithm for stream data, which exploits coresets in the sliding window model. We provide an experimental analysis of these hyper-parameters on the performance of the algorithm.
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