In this paper, we demonstrate a data-driven methodology for modelling the local similarity measures of various attributes in a dataset. We analyse the spread in the numerical attributes and estimate their distribution using polynomial function to showcase an approach for deriving strong initial value ranges of numerical attributes and use a non-overlapping distribution for categorical attributes such that the entire similarity range [0,1] is utilized. We use an open source dataset for demonstrating modelling and development of the similarity measures and will present a case-based reasoning (CBR) system that can be used to search for the most relevant similar cases.
CITATION STYLE
Verma, D., Bach, K., & Mork, P. J. (2019). Similarity measure development for case-based reasoning–a data-driven approach. In Communications in Computer and Information Science (Vol. 1056 CCIS, pp. 143–148). Springer. https://doi.org/10.1007/978-3-030-35664-4_14
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