Title :
Research on an improved algorithm for cluster analysis
Author :
Yong, Shi ; Ge, Zhang
Author_Institution :
Sch. of Accounting, Zhongnan Univ. of Econ. & Law, Wuhan, China
Abstract :
Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. In addition, cluster analysis usually acts as the preprocessing of other data mining operations. Therefore, cluster analysis has become a very active research topic in data mining. By improving the algorithm of classical Q-mode factor model, we put forward a new clustering method for large-scaled database: Q-Mode Factor Clustering Method, which dramatically reduce the time complexity of the algorithm.
Keywords :
computational complexity; data mining; database management systems; pattern clustering; classical Q-mode factor model; cluster analysis; data distribution; data mining technique; data segmentation; large-scaled database; pattern information; time complexity; Algorithm design and analysis; Clustering algorithms; Clustering methods; Complexity theory; Data mining; Eigenvalues and eigenfunctions; Q factor; Cluster analysis; Data mining; Factor analysis;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on
Conference_Location :
XianNing
Print_ISBN :
978-1-61284-458-9
DOI :
10.1109/CECNET.2011.5768863
