Title :
A text mining model based on improved density clustering algorithm
Author :
Chen Qi ; Lu JianFeng ; Zhang Hao
Author_Institution :
CIMS Center of Tongji Univ., Shanghai, China
Abstract :
The clustering algorithm based on density is widely used on text mining model, for example the DBSCAN(density-based spatial clustering of application with noise) algorithm. DBSCAN algorithm is sensitive in choose of parameters, it is hard to find suitable parameters. In this paper a method based on k-means algorithm is introduced to estimate the ε neighborhood and minpts. Finally an example is given to show the effectiveness of this algorithm.
Keywords :
data mining; pattern clustering; text analysis; DBSCAN; density based spatial clustering of application with noise algorithm; improved density clustering algorithm; k-means algorithm; text mining model; Art; Bayes methods; Clustering algorithms; Support vector machines; clustering algorithm; density clustering; text mining;
Conference_Titel :
Electronics Information and Emergency Communication (ICEIEC), 2013 IEEE 4th International Conference on
Conference_Location :
Beijing
DOI :
10.1109/ICEIEC.2013.6835520
