When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
M-Kernel Merging: Towards Density Estimation over Data Streams
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Enhancing principal direction divisive clustering
Pattern Recognition
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Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex for clustering. As such there is a growing need for algorithms that can be applied on streaming data and the at same time can cope with high dimensionality. To this end, here we design a streaming clustering approach by extending a recently proposed high dimensional clustering algorithm.