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Component-based Feature Saliency for Clustering

Hong, Xin, Li, Hailin, Miller, Paul, Zhou, Jianjiang, Li, Ling, Crookes, Danny, Lu, Yonggang, Li, Xuelong, Zhou, Huiyu (2019) Component-based Feature Saliency for Clustering. IEEE Transactions on Knowledge and Data Engineering, . ISSN 1041-4347. (doi:10.1109/TKDE.2019.2936847)

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https://doi.org/10.1109/TKDE.2019.2936847

Abstract

Simultaneous feature selection and clustering is a major challenge in unsupervised learning. In particular, there has been significant research into saliency measures for features that result in good clustering. However, as datasets become larger and more complex, there is a need to adopt a finer-grained approach to saliency by measuring it in relation to a part of a model. Another issue is learning the feature saliency and advanced model parameters. We address the first by presenting a novel Gaussian mixture model, which explicitly models the dependency of individual mixture components on each feature giving a new component-based feature saliency measure. For the second, we use Markov Chain Monte Carlo sampling to estimate the model and hidden variables. Using a synthetic dataset, we demonstrate the superiority of our approach, in terms of clustering accuracy and model parameter estimation, over an approach using a model-based feature saliency with expectation maximisation. We performed an evaluation of our approach with six synthetic trajectory datasets. To demonstrate the generality of our approach, we applied it to a network traffic flow dataset for intrusion detection. Finally, we performed a comparison with state-of-the-art clustering techniques using three real-world trajectory datasets of vehicle traffic.

Item Type: Article
DOI/Identification number: 10.1109/TKDE.2019.2936847
Subjects: Q Science
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 31 Oct 2019 00:55 UTC
Last Modified: 01 Nov 2019 13:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/78093 (The current URI for this page, for reference purposes)
Li, Ling: https://orcid.org/0000-0002-4026-0216
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