Abstract
Clustering analysis is well-used in data mining to group a set of observations into clusters according to their similarity, thus, the (dis)similarity measure between observations becomes a key feature for clustering analysis. However, classical clustering analysis algorithms cannot deal with observation contains both data and vague concepts by using traditional distance measures. In this paper, we proposed a novel (dis)similarity measure based on a prototype theory interpreted knowledge representation framework named label semantics. The new proposed measure is used to extend classical K-means algorithm for clustering data instances and the vague concepts represented by logical expressions of linguistic labels. The effectiveness of proposed measure is verified by experimental results on an image clustering problem, this measure can also be extended to cluster data and vague concepts represented by other granularities.
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Zhao, H., Qin, Z. (2015). Clustering Data and Vague Concepts Using Prototype Theory Interpreted Label Semantics. In: Huynh, VN., Inuiguchi, M., Demoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science(), vol 9376. Springer, Cham. https://doi.org/10.1007/978-3-319-25135-6_23
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DOI: https://doi.org/10.1007/978-3-319-25135-6_23
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Online ISBN: 978-3-319-25135-6
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