EIDBSCAN: An Extended Improving DBSCAN algorithm with sampling techniques
作者： Cheng Fa Tsai , Chun Yi Sung
DOI: 10.1504/IJBIDM.2010.030301
关键词:
摘要: 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 sampling-based DBSCAN algorithm. Additionally, the proposed algorithm may reduce eight Marked Boundary Objects to add seeds according far centrifugal force, which increases coverage. Our experimental results reveal yields more accurate results. In addition, all cases we studied, has lower execution time cost than several existing well-known approaches, such as DBSCAN, IDBSCAN KIDBSCAN algorithms.
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