dc.contributor.author | Verma, Deepika | |
dc.contributor.author | Bach, Kerstin | |
dc.contributor.author | Mork, Paul Jarle | |
dc.date.accessioned | 2020-01-30T09:23:36Z | |
dc.date.available | 2020-01-30T09:23:36Z | |
dc.date.created | 2020-01-15T11:01:52Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 978-3-030-35663-7 | |
dc.identifier.uri | http://hdl.handle.net/11250/2638790 | |
dc.description.abstract | 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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer Nature | nb_NO |
dc.title | Similarity Measure Development for Case-Based Reasoning–A Data-Driven Approach | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 6 | nb_NO |
dc.identifier.doi | 10.1007/978-3-030-35664-4_14 | |
dc.identifier.cristin | 1773475 | |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article. Locked until 22.11.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-35664-4_14 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitcode | 194,65,20,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.unitname | Institutt for samfunnsmedisin og sykepleie | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |