Efficient incremental density-based algorithm for clustering large datasets

In dynamic information environments such as the web, the amount of information is rapidly increasing. Thus, the need to organize such information in an efficient manner is more important than ever. With such dynamic nature, incremental clustering algorithms are always preferred compared to traditional static algorithms. In this paper, an enhanced version of the incremental DBSCAN algorithm is introduced for incrementally building and updating arbitrary shaped clusters in large datasets. The proposed algorithm enhances the incremental clustering process by limiting the search space to partitions rather than the whole dataset which results in significant improvements in the performance compared to relevant incremental clustering algorithms. Experimental results with datasets of different sizes and dimensions show that the proposed algorithm speeds up the incremental clustering process by factor up to 3.2 compared to existing incremental algorithms. (C) 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.