Abstract
In Chapter 1 we mentioned that two types of data, object (X) and relational (R), are used for numerical pattern recognition. Relational methods for classifier design are not as well developed as methods for object data. The most compelling reason for this is probably that sensors collect object data. Moreover, when each object is not represented by a feature vector, the problem of feature analysis is non-existent. Consequently, the models in this chapter deal exclusively with clustering. There are many applications that depend on clustering relational data — e.g., information retrieval, data mining in relational databases, and numerical taxonomy, so methods in this category are important. Several network methods for relational pattern recognition are given in Chapter 5.
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Bezdek, J.C., Keller, J., Krisnapuram, R., Pal, N.R. (1999). Cluster Analysis for Relational Data. In: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. The Handbooks of Fuzzy Sets Series, vol 4. Springer, Boston, MA. https://doi.org/10.1007/0-387-24579-0_3
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DOI: https://doi.org/10.1007/0-387-24579-0_3
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