Implementation of the D-Stream clustering algorithm for use in MOA. Now included as part of the MOA 17.06 release.
The D-Stream algorithm is described by Yixin Chen and Li Tu in their paper "Density-Based Clustering for Real-Time Stream Data" [2]. Please cite that paper if you use this code.
Nominal data should be used. Although basic support for numeric data is included, the original paper preprocesses numeric data to support the decomposition of each dimension into density grids.
MOA (Massive Online Analysis) [1] is a Java-based, open source framework for data stream mining. More details can be found on its website and it can be found on GitHub as well (https://github.com/waikato/moa).
[1] A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, “Moa: Massive online analysis,” J. Mach. Learn. Res., vol. 11, no. May, pp. 1601–1604, 2010.
[2] Y. Chen and L. Tu, “Density-Based Clustering for Real-Time Stream Data,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 133–142. DOI: 10.1145/1281192.1281210