Title :
Dependent function interval parameters training algorithm based on DBSCAN clustering
Author :
Yang, Li ; Guangqiang, Xie ; Xiaomei, Li ; Hua, Liu
Author_Institution :
Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
Dependent function is used to describe basic-element have a nature in what degree in domain, the interval parameters of dependent function decide the boundery value by which element change from quantitative to qualitative. This paper research on cleaning noise data and clustering with DBSCAN algorhithm based on a set of training samples without regard to subjective factors and computing the interval parameter with clustering result. In the paper, we have two simulations on experiment data and actual data taking the case of elementary dependent function, the simulation results are considerably accurate and reasonable.
Keywords :
data analysis; pattern clustering; DBSCAN algorithm; DBSCAN clustering; boundery value; data clustering; dependent function interval parameter training algorithm; elementary dependent function; noise data cleaning; subjective factor; Automation; Clustering algorithms; Computers; Educational institutions; Electronic mail; Hydroelectric power generation; Training; DBSCAN; Extenics; dependent function; parameter training;
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
Print_ISBN :
978-1-4673-2581-3