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Maximum Clusterability Divisive Clustering

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Maximum Clusterability Divisive Clustering. / Hofmeyr, David; Pavlidis, Nicos Georgios.
Computational Intelligence, 2015 IEEE Symposium Series on . Cape Town: IEEE, 2015. p. 780-786.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Hofmeyr, D & Pavlidis, NG 2015, Maximum Clusterability Divisive Clustering. in Computational Intelligence, 2015 IEEE Symposium Series on . IEEE, Cape Town, pp. 780-786. https://doi.org/10.1109/SSCI.2015.116

APA

Hofmeyr, D., & Pavlidis, N. G. (2015). Maximum Clusterability Divisive Clustering. In Computational Intelligence, 2015 IEEE Symposium Series on (pp. 780-786). IEEE. https://doi.org/10.1109/SSCI.2015.116

Vancouver

Hofmeyr D, Pavlidis NG. Maximum Clusterability Divisive Clustering. In Computational Intelligence, 2015 IEEE Symposium Series on . Cape Town: IEEE. 2015. p. 780-786 doi: 10.1109/SSCI.2015.116

Author

Hofmeyr, David ; Pavlidis, Nicos Georgios. / Maximum Clusterability Divisive Clustering. Computational Intelligence, 2015 IEEE Symposium Series on . Cape Town : IEEE, 2015. pp. 780-786

Bibtex

@inproceedings{863910c290a146a7a5fc24038922e3ae,
title = "Maximum Clusterability Divisive Clustering",
abstract = "The notion of cluster ability is often used to determine how strong the cluster structure within a set of data is, as well as to assess the quality of a clustering model. In multivariate applications, however, the cluster ability of a data set can be obscured by irrelevant or noisy features. We study the problem of finding low dimensional projections which maximise the cluster ability of a data set. In particular, we seek low dimensional representations of the data which maximise the quality of a binary partition. We use this bi-partitioning recursively to generate high quality clustering models. We illustrate the improvement over standard dimension reduction and clustering techniques, and evaluate our method in experiments on real and simulated data sets.",
author = "David Hofmeyr and Pavlidis, {Nicos Georgios}",
year = "2015",
month = dec,
day = "7",
doi = "10.1109/SSCI.2015.116",
language = "English",
isbn = "9781479975600 ",
pages = "780--786",
booktitle = "Computational Intelligence, 2015 IEEE Symposium Series on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Maximum Clusterability Divisive Clustering

AU - Hofmeyr, David

AU - Pavlidis, Nicos Georgios

PY - 2015/12/7

Y1 - 2015/12/7

N2 - The notion of cluster ability is often used to determine how strong the cluster structure within a set of data is, as well as to assess the quality of a clustering model. In multivariate applications, however, the cluster ability of a data set can be obscured by irrelevant or noisy features. We study the problem of finding low dimensional projections which maximise the cluster ability of a data set. In particular, we seek low dimensional representations of the data which maximise the quality of a binary partition. We use this bi-partitioning recursively to generate high quality clustering models. We illustrate the improvement over standard dimension reduction and clustering techniques, and evaluate our method in experiments on real and simulated data sets.

AB - The notion of cluster ability is often used to determine how strong the cluster structure within a set of data is, as well as to assess the quality of a clustering model. In multivariate applications, however, the cluster ability of a data set can be obscured by irrelevant or noisy features. We study the problem of finding low dimensional projections which maximise the cluster ability of a data set. In particular, we seek low dimensional representations of the data which maximise the quality of a binary partition. We use this bi-partitioning recursively to generate high quality clustering models. We illustrate the improvement over standard dimension reduction and clustering techniques, and evaluate our method in experiments on real and simulated data sets.

U2 - 10.1109/SSCI.2015.116

DO - 10.1109/SSCI.2015.116

M3 - Conference contribution/Paper

SN - 9781479975600

SP - 780

EP - 786

BT - Computational Intelligence, 2015 IEEE Symposium Series on

PB - IEEE

CY - Cape Town

ER -