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

## TL;DR

This paper presents a data-driven method for developing similarity measures in case-based reasoning systems, focusing on modeling attribute distributions to improve case retrieval accuracy.

## Contribution

It introduces a novel approach for modeling local similarity measures using distribution analysis, enhancing the effectiveness of CBR systems.

## Key findings

- Effective modeling of numerical attribute distributions
- Utilization of non-overlapping categorical attribute distributions
- Demonstration on an open source dataset

## 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.