HDenStream

Data stream clustering is an importance issue in data stream mining. In most of the existing algorithms, only the continuous features are used for clustering. In this paper, we introduce an algorithm HDenStream for clustering data stream with heterogeneous features. The HDenstream is also a density-based algorithm, so it is capable enough to cluster arbitrary shapes and handle outliers. Theoretic analysis and experimental results show that HDenStream is effective and efficient.

DenStreamClustering

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Initial contribute: 2021-01-09

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● Method-focused categoriesData-perspectiveGeoinformation analysis

Detailed Description

English {{currentDetailLanguage}} English

Below are quoted from: Lin, Jinxian, and Hui Lin. "A density-based clustering over evolving heterogeneous data stream." 2009 ISECS international colloquium on computing, communication, control, and management. Vol. 4. IEEE, 2009.

Data stream clustering is an importance issue in data stream mining. In most of the existing algorithms, only the continuous features are used for clustering. In this paper, we introduce an algorithm HDenStream for clustering data stream with heterogeneous features. The HDenstream is also a density-based algorithm, so it is capable enough to cluster arbitrary shapes and handle outliers. Theoretic analysis and experimental results show that HDenStream is effective and efficient.

Algorithm HDenStream (D,β,u,λ,ε)

Algorithm

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Jie Song (2021). HDenStream, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/f27e0a7c-216a-47de-bd4a-1e98322575f0
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Contributor(s)
Initial contribute : 2021-01-09

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