Abstract
The aim of this paper is to study the concept of inductive clustering and two approximations in nearest neighbor clustering induced thereby. The concept of inductive clustering means that natural classification rules are derived as the results of clustering, a typical example of which is the Voronoi regions in K-means clustering. When the rule of nearest prototype allocation in K-means is replaced by nearest neighbor classification, we have inductive clustering related to the single linkage in agglomerative hierarchical clustering. The latter method naturally derives two approximations that can be compared to lower and upper approximations for rough sets. We thus have a method of inductive clustering with twofold approximations related to nearest neighbor classification. Illustrative examples show implications and significances of this concept.
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Miyamoto, S., Takumi, S. (2012). Inductive Clustering and Twofold Approximations in Nearest Neighbor Clustering. In: Torra, V., Narukawa, Y., López, B., Villaret, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2012. Lecture Notes in Computer Science(), vol 7647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34620-0_32
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DOI: https://doi.org/10.1007/978-3-642-34620-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34619-4
Online ISBN: 978-3-642-34620-0
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