EIDBSCAN: An Extended Improving DBSCAN algorithm with sampling techniques
Cheng-Fa Tsai, Chun-Yi Sung · International Journal of Business Intelligence and Data Mining · 2009
Cluster analysis in data mining and knowledge discovery is an essential business application. This investigation describes a new clustering approach named EIDBSCAN that extends expansion seed selection into a sampling-based DBSCAN clustering algorithm. Additionally, the proposed algorithm may reduce eight Marked Boundary Objects to add expansion seeds according to far centrifugal force, which increases coverage. Our experimental results reveal that the proposed EIDBSCAN yields more accurate clustering results. In addition, in all the cases we studied, the proposed approach has a lower execution time cost than several existing well-known approaches, such as DBSCAN, IDBSCAN and KIDBSCAN clustering algorithms.