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Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

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conference contribution
posted on 06.10.2020 by D O'Neill, Andrew Lensen, Bing Xue, Mengjie Zhang
© 2018 IEEE. Clustering, an important unsupervised learning task, is very challenging on high-dimensional data, since the generated clusters can be significantly less meaningful as the number of features increases. Feature selection and/or feature weighting can address this issue by selecting and weighting only informative features. These techniques have been extensively studied in supervised learning, e.g. classification, but they are very difficult to use with clustering due to the lack of effective similarity/distance and validation measures. This paper utilises the powerful global search ability of particle swarm optimisation (PSO) on continuous problems, to propose a PSO based method for simultaneous feature selection and feature weighting for clustering on high-dimensional data, where a new validation measure is also proposed as the fitness function of the PSO method. Experiments on datasets with varying dimensionalities and different number of known clusters show that the proposed method can successfully improve clustering performance of different types of clustering algorithms over using the baseline of the original feature set.

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Preferred citation

O'Neill, D., Lensen, A., Xue, B. & Zhang, M. (2018, September). Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, BRAZIL (00 pp. 173-180). IEEE. https://doi.org/10.1109/CEC.2018.8477974

Conference name

2018 IEEE Congress on Evolutionary Computation (CEC)

Conference Place

Rio de Janeiro, BRAZIL

Conference start date

08/07/2018

Conference finish date

13/07/2018

Title of proceedings

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Volume

00

Series

IEEE Congress on Evolutionary Computation

Publication or Presentation Year

28/09/2018

Pagination

173-180

Publisher

IEEE

Publication status

Published

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