Abstract
GbDBSCAN (an efficient grid-based DBSCAN algorithm) is an excellent improved DBSCAN algorithm, which makes up the defects that DBSCAN algorithm is sensitive to clustering parameters and unable to deal with large database, and retains the advantage of separating noise and finding arbitrary shape clusters. However, in GbDBSCAN, the grid technique treats the total number of points in one grid as the grid dense, and this simple treatment will depress the clustering accuracy. Therefore, CGDBSCAN is proposed in this paper, and within it 'migration-coefficient' conception is presented firstly. With the optimization effect of contribution and migration-coefficient, the optimal selection of parameter Eps and the efficient SP-tree query index, the accuracy of clustering result is effectively improved while ensuring the operational efficiency of this algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
