DocumentCode
1806087
Title
Research on a new clustering algorithm in data mining
Author
Tan Zhongbing
Author_Institution
Computer Science Department, Beijing Institute of Technology, Zhuhai, China
fYear
2013
fDate
1-8 Jan. 2013
Firstpage
1
Lastpage
4
Abstract
Data mining is one of the leading fields in the combination area of database and decision supporting, and clustering is a significant task for data mining, in which clustering algorithm is the core technology. The new clustering method based on genetic algorithm and gradient descent method (G-G clustering algorithm) is proposed in this paper. Genetic algorithm has the advantages of global searching and strong robustness, and will not getting stuck at local optimal values. Unfortunately, it can only reach the near-optimal value after many generations of selection, crossover and mutation. Therefore, gradient descent method is utilized at the end of genetic algorithm based clustering method to get global optimal values. Clustering results of two groups of experimental data show that the new clustering method is one with global optimal, and the results is evidently better than k-means clustering method.
Keywords
Algorithm design and analysis; Clustering algorithms; Wheels; clustering analysis; data mining; genetic algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Conference Anthology, IEEE
Conference_Location
China
Type
conf
DOI
10.1109/ANTHOLOGY.2013.6784990
Filename
6784990
Link To Document