Data

Incremental clustering of dynamic data streams using connectivity based representative points

Curtin University
Mihai Lazarescu (Managed by) Patrick Peursum (hasAssociatonWith, isRelatedTo)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://ddfe.curtin.edu.au/impca/ic/&rft.title=Incremental clustering of dynamic data streams using connectivity based representative points&rft.publisher=Curtin University&rft.description=RepStream is a graph-based stream clustering algorithm utilising representative points, reciprocal linkage, density evaluation to perform clustering. This provided implementation is written in C and compiles under Unix-based systems. (Special thanks to Dinh Q. Phung for the DBSCAN implementation used in this research.)&rft.creator=Anonymous&rft.date=2013&rft.relation=http://dx.doi.org/10.1016/j.datak.2008.08.006&rft.relation=http://dx.doi.org/10.1007/978-3-540-68125-0_62&rft_subject=Data mining&rft_subject=Clustering&rft_subject=Pattern Recognition and Data Mining&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING&rft_subject=Stream clustering&rft.type=dataset&rft.language=English Access the data
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The software is provided freely for research-only purposes under GPL V2 license.


Brief description

RepStream is a graph-based stream clustering algorithm utilising representative points, reciprocal linkage, density evaluation to perform clustering. This provided implementation is written in C and compiles under Unix-based systems. (Special thanks to Dinh Q. Phung for the DBSCAN implementation used in this research.)

Data time period: 2007 to 2008

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Subjects
Artificial Intelligence and Image Processing | Clustering | Data mining | Information and Computing Sciences | Pattern Recognition and Data Mining | Stream clustering |

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