Title :
A Text Clustering Algorithm Combining K-Means and Local Search Mechanism
Author :
Cheng, Lanlan ; Sun, Yueheng ; Wei, Jinghui
Author_Institution :
Sch. of Comput. Sci. & Inf. Eng., Tianjin Univ. of Sci. & Technol., Tianjin, China
Abstract :
Text clustering is one of common techniques in mining large scales of document data. The paper presents an improved K-means text clustering algorithm in which a local search mechanism is introduced. By the iteration process of K-means algorithm, our approach can quickly get a local extreme point, and then use the search strategy of local search mechanism to have K-means jump out of that point and get a better solution. The experimental results show that our approach achieves better performance in the terms of entropy than the traditional algorithm while not slowing down the clustering speed.
Keywords :
data mining; entropy; iterative methods; pattern clustering; search problems; text analysis; K-means text clustering algorithm; document data mining; entropy; iteration process; local search mechanism; Clustering algorithms; Computer science; Data engineering; Data mining; Electronic mail; Entropy; Iterative algorithms; Large-scale systems; Partitioning algorithms; Sun; K-Means; local search mechanism; text clustering;
Conference_Titel :
Research Challenges in Computer Science, 2009. ICRCCS '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3927-0
Electronic_ISBN :
978-1-4244-5410-5
DOI :
10.1109/ICRCCS.2009.21