Year: 2015
Type: article
Abstract: 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.
Source: Alexandria Engineering Journal
Institution: Alexandria University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,900
OpenAlex ID: W2111843750