The $\mathcal{E}$-Average Common Submatrix: Approximate Searching in a Restricted Neighborhood
This is an incremental improvement for researchers in pattern matching and information retrieval.
The paper tackles the problem of measuring similarity between 2D arrays by introducing a new dissimilarity measure that extends the Average Common Submatrix, achieving better performance with low execution time and larger information retrieval.
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.