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