Title :
A method of dynamically determining the number of clusters and cluster centers
Author :
Shao Xiongkai ; Pi Jing ; Liu lianzhou
Author_Institution :
Sch. of Comput. Sci., Hubei Univ. of Technol., Wuhan, China
Abstract :
Text clustering is an important technology in the field of data mining. The traditional K-means algorithm is sensitive to the number of clusters, and there is a limitation that the result of randomly initializing cluster centers is not stable. This paper presents a method of dynamically determining the number of clusters and cluster centers based on text similarity matrix. The experiment results show that the method works well and improves the K-means algorithm´s accuracy and adaptability.
Keywords :
data mining; pattern clustering; text analysis; K-means algorithm; cluster centers; data mining; text clustering; text similarity matrix; Clustering algorithms; Computational modeling; Computers; dynamically determining the number of clusters; k-means algorithm; text similarity;
Conference_Titel :
Computer Science & Education (ICCSE), 2013 8th International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4673-4464-7
DOI :
10.1109/ICCSE.2013.6553925
