Image Clustering Acceleration: A Cuckoo Search-Enhanced K-Means Algorithm

In the landscape of clustering algorithms, K-means stands out for its straightforward approach and computational effectiveness. However, its reliance on the initial selection of cluster centers renders it vulnerable to entrapment in local optima, undermining its clustering efficacy. This study introduces an enhanced K-means algorithm, fortified by the integration of the cuckoo search optimization technique. This novel hybrid algorithm leverages the cuckoo search's ability to navigate the solution space effectively, thereby identifying optimal initial cluster centroids for the K-means algorithm. Empirical evaluation demonstrates that the amalgamated algorithm exhibits superior clustering performance, successfully circumventing the pitfalls of local optima that afflict the traditional K-means approach.