Maximum Clusterability Divisive Clustering

Hofmeyr, David and Pavlidis, Nicos Georgios (2015) Maximum Clusterability Divisive Clustering. In: Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, Cape Town, pp. 780-786. ISBN 9781479975600

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
81216
Deposited By:
Deposited On:
22 Aug 2016 10:38
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Sep 2020 07:38