Title :
Reckon the Parameter of DBSCAN for Multi-density Data Sets with Constraints
Author :
Huang, Tian-qiang ; Yu, Yang-qiang ; Li, Kai ; Zeng, Wen-fu
Author_Institution :
Dept. of Comput. Sci., Fujian Normal Univ., Fuzhou, China
Abstract :
DBSCAN is a typical density-based clustering algorithm, but it is time-consuming to ascertain the parameter Eps and it does not perform well on multi-density datasets because of the global parameter Eps. In this paper, we use must-link constraints to ascertain the parameter Eps for each density distribution effectively and automatically, which will be used to deal with multi-density data sets for traditional DBSCAN algorithm. Experimental results reveal that our algorithm can reckon the parameter of DBSCAN for multi-density data sets with constraints effectively.
Keywords :
pattern clustering; DBSCAN; density distribution; density-based clustering algorithm; multidensity data sets; must-link constraints; Artificial intelligence; Clustering algorithms; Clustering methods; Computational intelligence; Computer science; Data mining; Databases; Mathematics; Shape; DBSCAN; Multi-density; must-link constraint;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.393