HYBRID GENETIC K-MEANS ASSISTED DENSITY BASED CLUSTERING ALGORITHM

The data mining algorithms performance is key issue, when the data becomes more and more. Clustering analysis is a dynamic and challenge research direction in the area of data mining for compound data samples. DBSCAN is a density based clustering algorithm with numerous advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. ????(???? 2 ) making it difficult for practical applications especially with large complex data samples. Therefore, this paper suggested a hybrid approach to minimize the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based Kmeans partition algorithm. The scientific experiments showed that the proposed hybrid approach obtains competitive results when compared with the traditional approach and significantly improves the computational time.