Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering-Reference-Cited by-同舟云学术

Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering

Published:2017-06-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th International Conference on Scientific and Statistical Database Management
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Author:

Lu Yifeng¹,Hassani Marwan²,Seidl Thomas¹

Affiliation:

1. Institute for Informatics, Ludwig-Maximilians-Universität, Munich, Germany

2. Architecture of Information Systems Group, Eindhoven University of Technology, Eindhoven, Netherlands

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3085504.3085511

Reference30 articles.

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2. Maintaining knowledge about temporal intervals

3. Feng Cao Martin Ester Weining Qian and Aoying Zhou. 2006. Density-Based Clustering over an Evolving Data Stream with Noise. In SDM. SIAM 328--339. Feng Cao Martin Ester Weining Qian and Aoying Zhou. 2006. Density-Based Clustering over an Evolving Data Stream with Noise. In SDM. SIAM 328--339.

4. Chung-i Chang and Nancy P Lin. 2009. Sequential Patterns Mining with Fuzzy Time-Intervals. ICSAI (2009) 165--169. Chung-i Chang and Nancy P Lin. 2009. Sequential Patterns Mining with Fuzzy Time-Intervals. ICSAI (2009) 165--169.

5. SeqStream: Mining Closed Sequential Patterns over Stream Sliding Windows

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