Automatic Clustering for Selection of Optimal Number of Clusters by K-Means Integrated with Enhanced Firefly Algorithms

Unsupervised machine learning includes data clustering, which discovers and comprehends underlying data patterns as well as classifies objects within a dataset based on similarity metrics such as Davies-Bouldin (DB) and CompactSeparatedValues (CSV) (CS). The most well-known and powerful partitional clustering algorithm is the K-means clustering algorithm, which is one of the top ten most frequently used data mining algorithms. We have combined the firefly metaheuristic optimization algorithm with K-means clustering to enhance the K-means for automatic clustering in our paper. The experimental results show that the hybridized algorithm KFA outperforms the k-means algorithm than PSO, KPSO, and K-means in terms of efficiency.