# The $\mathcal{E}$-Average Common Submatrix: Approximate Searching in a Restricted Neighborhood

## TL;DR

This paper proposes a new similarity measure for 2D arrays that improves efficiency and effectiveness by focusing on pattern frequency, local neighborhoods, and approximate matching, enhancing information retrieval tasks.

## Contribution

It introduces a novel (dis)similarity measure for 2D arrays that combines pattern frequency, neighborhood restriction, and approximate matching, advancing current methods.

## Key findings

- Achieves better performance with lower execution time.
- Enhances information retrieval accuracy.
- Effective in approximate pattern matching in 2D arrays.

## Abstract

This paper introduces a new (dis)similarity measure for 2D arrays, extending the Average Common Submatrix measure. This is accomplished by: (i) considering the frequency of matching patterns, (ii) restricting the pattern matching to a fixed-size neighborhood, and (iii) computing a distance-based approximate matching. This will achieve better performances with low execution time and larger information retrieval.