  K-means Clustering is the most well-known partitioning algorithm among all
clustering, by which we can partition the data objects very easily in to more
than one clusters. However, for K-means to choose an appropriate number of
clusters without any prior domain knowledge about the dataset is challenging,
especially in high-dimensional data objects. Hence, we have implemented the
Silhouette and Elbow methods with PCA to find an optimal number of clusters.
Also, previously, so many meta-heuristic swarm intelligence algorithms inspired
by nature have been employed to handle the automatic data clustering problem.
Firefly is efficient and robust for automatic clustering. However, in the
Firefly algorithm, the entire population is automatically subdivided into
sub-populations that decrease the convergence rate speed and trapping to local
minima in high-dimensional optimization problems. Thus, our study proposed an
enhanced firefly, i.e., a hybridized K-means with an ODFA model for automatic
clustering. The experimental part shows output and graphs of the Silhouette and
Elbow methods as well as the Firefly algorithm
