dc.contributor.author | Verma, Deepika | |
dc.contributor.author | Bach, Kerstin | |
dc.contributor.author | Mork, Paul Jarle | |
dc.date.accessioned | 2020-05-18T07:26:41Z | |
dc.date.available | 2020-05-18T07:26:41Z | |
dc.date.created | 2020-02-18T16:18:16Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Communications in Computer and Information Science. 2019, 1056 CCIS 143-148. | en_US |
dc.identifier.issn | 1865-0929 | |
dc.identifier.uri | https://hdl.handle.net/11250/2654705 | |
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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.title | Similarity measure development for case-based reasoning?a data-driven approach | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.source.pagenumber | 143-148 | en_US |
dc.source.volume | 1056 CCIS | en_US |
dc.source.journal | Communications in Computer and Information Science | en_US |
dc.identifier.doi | 10.1007/978-3-030-35664-4_14 | |
dc.identifier.cristin | 1795523 | |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35664-4_14 | en_US |
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 | |