G-OPTICS: fast ordering density-based cluster objects using graphics processing units

摘要

Clustering is the process of forming groups or clusters of similar objects in the dataset and has been used as an important tool for many data mining applications including the web-based ones. While density-based clustering algorithms are widely adopted, their clustering result is highly sensitive to parameter values. The OPTICS algorithm presents a solution to this problem; it produces an ordering of objects that is equivalent to the clustering results for a wide range of thresholds E. In this paper, we propose an algorithm named G-OPTICS to significantly improve the performance of OPTICS using a graphics processing unit (GPU). The experimental results using real and synthetic datasets demonstrated that G-OPTICS outperformed the previously fastest FOPTICS algorithm by up to 118.3 times (67.7 times on the average).