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arXiv · 1905.08581

Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach

Deepika Verma·Kerstin Bach·Paul Jarle Mork
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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.

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BibTeXRIS

Deepika Verma, Kerstin Bach, Paul Jarle Mork. 2019-05-21. Similarity Measure Development for Case-Based Reasoning- A Data-driven Approach. https://arxiv.org/abs/1905.08581

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