Title :
An Improved K-Means Text Clustering Algorithm Based on Local Search
Author_Institution :
Dept. of MIS, Tianjin Univ. of Finance & Econ., Tianjin
Abstract :
This paper presents a new text clustering algorithm based on local search and K-means (LSKM) and explains the characteristic of this algorithm theoretically. The experimental results of three standard test collections show that the new algorithm is better than K-means and the theoretical analysis is also testified in this paper.
Keywords :
learning (artificial intelligence); pattern clustering; search problems; text analysis; K-means text clustering algorithm; combinatorial optimization problem; local search; unsupervised learning; Algorithm design and analysis; Clustering algorithms; Finance; Partitioning algorithms; Testing; Unsupervised learning;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.2693
