Clustering Using Genetic Algorithm: A Collaborative Performance Analysis

Clustering is an essential unsupervised classification technique. When data is unlabeled, K-means, an unsupervised learning technique, is used because of its simplicity. This technique has the problem of getting plunges at values that are not optimal. A genetic algorithm (GA) removes the shortcoming of the K-Mean Algorithm. Genetic algorithms are optimization techniques with a randomized search that follow a natural genetic's nature, with implicit parallelism in a large amount. GA digs into large, multimodal, complex landscapes to provide optimal solutions. The proposed work uses GA with k-means to integrate both advantages. Results show that the proposed algorithm performs significantly superior to the widely used technique, that is K-means algorithm.