Dutta Baruah, Rashmi and Angelov, Plamen (2012) Evolving local means methods for clustering of streaming data. In: Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on. IEEE Press, pp. 2161-2168. ISBN 9781467315074Full text not available from this repository.
A new on-line evolving clustering approach for streaming data is proposed in this paper. The approach is based on the concept that local mean of samples within a region has the highest density and the gradient of the density points towards the local mean. The algorithm merely requires recursive calculation of local mean and variance, due to which it easily meets the memory and time constraints for data stream processing. The experimental results using synthetic and benchmark datasets show that the proposed approach attains results at par with offline approach and is comparable to popular density-based mean-shift clustering yet it is significantly more efficient being one-pass and non-iterative.
|Item Type:||Contribution in Book/Report/Proceedings|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||27 Jul 2012 09:57|
|Last Modified:||24 Mar 2017 02:09|
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