A Novel Hybrid Candidate Group Search Genetic Clustering for Large Scale Data

Clustering is an unsupervised approach to extract hidden patterns from the datasets. There are certain challenges in clustering, though it is very much difficult to produce good clustering, researchers have provided the solutions through various hybrid approaches. The proposed work is based on enhancing the clustering results by using two algorithms: First Candidate Group Search (CGS) is used to produce clusters and Genetic algorithm (GA). A CGS can be applied to large dataset with less computational time, but the drawback is it can’t results in global optima. Hence GA is used for further optimization. Both algorithms will produce optimized clusters