Title :
Research of data mining of clustering analysis based on improved genetic algorithm
Author :
Zhou, Yingjun ; YOU, Jianxin
Author_Institution :
Sch. of Econ. & Manage., Tongji Univ., Shanghai, China
Abstract :
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.This paper presents a new improved genetic algorithm by means of operations of adaptive crossover and adaptive mutation. Experimental results demonstrate that the algorithm has greater global searching capability and can get better clustering.
Keywords :
data mining; genetic algorithms; pattern clustering; K-means algorithm; adaptive crossover; adaptive mutation; clustering analysis; data mining; genetic algorithm; Algorithm design and analysis; Clustering algorithms; Data mining; Economics; Genetic algorithms; Iris; Scheduling; clustering analysis; genetic algorithm;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
Conference_Location :
Hohhot
Print_ISBN :
978-1-4244-9436-1
DOI :
10.1109/MACE.2011.5988404
