Abstract
Streaming data clustering is becoming the most efficient way to cluster a very large data set. In this paper we present a new approach, called G-Stream, for topological clustering of evolving data streams. G-Stream allows one to discover clusters of arbitrary shape without any assumption on the number of clusters and by making one pass over the data. The topological structure is represented by a graph wherein each node represents a set of “close” data points and neighboring nodes are connected by edges. The use of the reservoir, to hold, temporarily, the very distant data points from the current prototypes, avoids needless movements of the nearest nodes to data points and therefore, improving the quality of clustering. The performance of the proposed algorithm is evaluated on both synthetic and real-world data sets.
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Ghesmoune, M., Azzag, H., Lebbah, M. (2014). G-Stream: Growing Neural Gas over Data Stream. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_26
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DOI: https://doi.org/10.1007/978-3-319-12637-1_26
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