Similarity measure development for case-based reasoning?a data-driven approach
Peer reviewed, Journal article
MetadataShow full item record
Original versionCommunications in Computer and Information Science. 2019, 1056 CCIS 143-148. 10.1007/978-3-030-35664-4_14
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.