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