Title :
An improved K-means clustering algorithm
Author :
Zhu, Jian ; Wang, Hanshi
Author_Institution :
Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing, China
Abstract :
That traditional K-mean algorithm is a widely used clustering algorithm, with a wide application. In light of the disadvantage of K-mean algorithm, improvement is made to the traditional K-mean algorithm, a k value learning algorithm is proposed. Using genetic algorithm to optimize the K value, and improve clustering performance.
Keywords :
genetic algorithms; pattern clustering; genetic algorithm; k value learning algorithm; k-means clustering algorithm; Application software; Clustering algorithms; Computer science; Data compression; Data mining; Euclidean distance; Genetic algorithms; Modeling; Neural networks; Radial basis function networks; Clustering algorithm; K-mean value; genetic algorithm;
Conference_Titel :
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5263-7
Electronic_ISBN :
978-1-4244-5265-1
DOI :
10.1109/ICIME.2010.5478087
