Component-based Feature Saliency for Clustering

Xin Hong, Paul Miller, Jianjiang Zhou, Ling Li, Daniel Crookes, Yonggang Lu, Xuelong Li, Huiyu Zhou

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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.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Early online date22 Aug 2019
DOIs
Publication statusEarly online date - 22 Aug 2019

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