One of the most important operations involving Data Mining patterns is computing their similarity. In this paper we present a general framework for comparing both simple and complex patterns, i.e., patterns built up from other patterns. Major features of our framework include the notion of structure and measure similarity, the possibility of managing multiple coupling types and aggregation logics, and the recursive definition of similarity for complex patterns. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Bartolini, I., Ciaccia, P., Ntoutsi, I., Patella, M., & Theodoridis, Y. (2004). A unified and flexible framework for comparing simple and complex patterns. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-30116-5_45
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