Hany . Hanafy
Enhanced Density Based Algorithm for Clustering Large Datasets
Clustering is one of the important data mining techniques for extracting knowledge from datasets in various applications. Most of the clustering algorithms do not achieve the majority of the clustering requirements as scalability discovering clusters of different shapes, dealing with noise, dealing with high dimensional data. In this thesis, an enhanced density based algorithm is proposed that improves fast density based clustering algorithm. The experimental results show that the new algorithm is superior to L-DBSCAN and fast density-based clustering and DBSCAN algorithms in more specific aspects, like running time, clusters’ compactness in terms of intra-cluster density and clusters’ separation in terms of inter-cluster density.