About: Component-based Feature Saliency for Clustering

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #78093 Component-based Feature Saliency for Clustering
sameAs	Component-based Feature Saliency for Clustering
http://eprints.org/ontology/hasAccepted	Component-based Feature Saliency for Clustering (PDF)
http://eprints.org/ontology/hasDocument	Component-based Feature Saliency for Clustering (PDF) Component-based Feature Saliency for Clustering (Other) Component-based Feature Saliency for Clustering (Other) Component-based Feature Saliency for Clustering (Other) Component-based Feature Saliency for Clustering (Other) Component-based Feature Saliency for Clustering (Other)
dc:hasVersion	Component-based Feature Saliency for Clustering (PDF)
Title	Component-based Feature Saliency for Clustering
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2019-08-22
Creator	Xuelong Li Xin Hong Jianjiang Zhou Yonggang Lu Danny Crookes Paul Miller Huiyu Zhou Hailin Li L. Li
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	IEEE Transactions on Knowledge and Data Engineering https://kar.kent.ac.uk/id/repository
Subject	Q Science
list of authors	https://kar.kent.ac.uk/id/eprint/78093#authors
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #78093 Component-based Feature Saliency for Clustering

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