Improving K-means clustering based on firefly algorithm
Amal Mahmood Naji Al Radhwani, Zakariya Yahya Algamal · Journal of Physics Conference Series · 2021
Abstract Data clustering determines a group of patterns in a dataset which are homogeneous in nature. The objective is to develop an automatic algorithm which can accurately classify an unleveled dataset into groups. The K-means method is the most fundamental partitioned clustering concept. However, the performance of K-means method is fully depending on determining the number of clusters, K, and determining the optimal centroid for performing the clustering process. In this paper, an adaptive firefly optimization algorithm, which is a nature-inspired algorithm, is employed to improve the K-means clustering. The experimental results of clustering two real datasets show that the proposed method is able to effectively outperform other alternatives methods.