Abstract
Structured data bases may include both numerical and non-numerical attributes (categorical or CA). Databases which include CAs are called “mixed” databases (MD). Metric clustering algorithms are ineffectual when presented with MDs because, in such algorithms, the similarity between the objects is determined by measuring the differences between them, in accordance with some predefined metric. Nevertheless, the information contained in the CAs of MDs is fundamental to understand and identify the patterns therein. A practical alternative is to encode the instances of the CAs numerically. To do this we must consider the fact that there is a limited subset of codes which will preserve the patterns in the MD. To identify such pattern-preserving codes (PPC) we appeal to a statistical methodology. It is possible to statistically identify a set of PPCs by selectively sampling a bounded number of codes (corresponding to the different instances of the CAs) and demanding the method to set the size of the sample dynamically. Two issues have to be considered for this method to be defined in practice: (a) How to set the size of the sample and (b) How to define the adequateness of the codes. In this paper we discuss the method and present a case of study wherein the appropriateness of the method is illustrated.
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Kuri-Morales, A. (2018). Pattern Discovery in Mixed Data Bases. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_18
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