Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 133, Issue -, Pages 17-24Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2014.01.017
Keywords
Spectral clustering; K-clustering; Hierarchical cluster analysis; Cluster validation; Electronic nose; Tomato juice
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Funding
- National Key Technology RD Program [2012BAD29B02-4]
- Chinese National Foundation of Nature and Science [31071548]
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Various clustering algorithms have been developed since conventional hierarchical cluster analysis (HCA) and partitioning clustering algorithms have their own limitations and scopes of applications. However, in the area of e-nose where clustering is applied, the conventional algorithms (mostly HCA) still play a dominant role. In addition, comparison among different clustering methods or validation of clustering results was seldom mentioned. In this paper, we present a state-of-the-art clustering method - spectral clustering - and compare it with six conventional clustering methods: K-clustering (ISODATA, FCM and k-means) and HCA (single linkage, complete linkage and Ward's). Three external validation criteria - mutual information criteria (MI), precision and rand index (RI) - were used to evaluate clustering performances on three independent e-nose datasets. The spectral clustering outperforms with statistical significance (alpha = 0.05) the performance of other methods, and the single linkage presents the worst (unacceptable) clustering result. In addition, the proposed approach - cluster validation criteria in combination with majority voting - in a way makes clustering a semi-supervised classification technique. Using this approach it is possible to compare clustering based semi-supervised methods with classification methods to find which method is better for discrimination of a certain e-nose dataset. (C) 2014 Elsevier B.V. All rights reserved.
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