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Evolving local means methods for clustering of streaming data

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Evolving local means methods for clustering of streaming data. / Dutta Baruah, Rashmi; Angelov, Plamen.
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on. IEEE Press, 2012. p. 2161-2168.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Dutta Baruah, R & Angelov, P 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. https://doi.org/10.1109/FUZZ-IEEE.2012.6251366

APA

Dutta Baruah, R., & Angelov, P. (2012). Evolving local means methods for clustering of streaming data. In Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on (pp. 2161-2168). IEEE Press. https://doi.org/10.1109/FUZZ-IEEE.2012.6251366

Vancouver

Dutta Baruah R, Angelov P. Evolving local means methods for clustering of streaming data. In Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on. IEEE Press. 2012. p. 2161-2168 doi: 10.1109/FUZZ-IEEE.2012.6251366

Author

Dutta Baruah, Rashmi ; Angelov, Plamen. / Evolving local means methods for clustering of streaming data. Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on. IEEE Press, 2012. pp. 2161-2168

Bibtex

@inproceedings{815c9dde745a4711b6a7ba61e86c4f7b,
title = "Evolving local means methods for clustering of streaming data",
abstract = "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.",
author = "{Dutta Baruah}, Rashmi and Plamen Angelov",
year = "2012",
month = jun,
day = "10",
doi = "10.1109/FUZZ-IEEE.2012.6251366",
language = "English",
isbn = "9781467315074",
pages = "2161--2168",
booktitle = "Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on",
publisher = "IEEE Press",

}

RIS

TY - GEN

T1 - Evolving local means methods for clustering of streaming data

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2012/6/10

Y1 - 2012/6/10

N2 - 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.

AB - 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.

U2 - 10.1109/FUZZ-IEEE.2012.6251366

DO - 10.1109/FUZZ-IEEE.2012.6251366

M3 - Conference contribution/Paper

SN - 9781467315074

SP - 2161

EP - 2168

BT - Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

PB - IEEE Press

ER -