TY - GEN
T1 - User analysis based on fuzzy clustering
AU - Ming, Yang
AU - Hong, Li
PY - 2009
Y1 - 2009
N2 - In order to solve the problem of user-classification to reflect the features of web users inflexible, a novel user classification model was presented in this paper. By introducing the concept of time discretization and applying fuzzy equivalence relation clustering to classify web users, the model can rationally solve the user classification problems. Empirical results showed that the output of user classification was not unique and the parameter δ should be adjusted based on applications. Compared to those hard clustering, this model is proved to be more effective to classify web users.
AB - In order to solve the problem of user-classification to reflect the features of web users inflexible, a novel user classification model was presented in this paper. By introducing the concept of time discretization and applying fuzzy equivalence relation clustering to classify web users, the model can rationally solve the user classification problems. Empirical results showed that the output of user classification was not unique and the parameter δ should be adjusted based on applications. Compared to those hard clustering, this model is proved to be more effective to classify web users.
KW - Fuzzy clustering method
KW - User classification
KW - Web-logs preprocessing
UR - https://www.scopus.com/pages/publications/71049179040
U2 - 10.1109/BIFE.2009.53
DO - 10.1109/BIFE.2009.53
M3 - 会议稿件
AN - SCOPUS:71049179040
SN - 9780769537054
T3 - 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
SP - 194
EP - 196
BT - 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
T2 - 2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009
Y2 - 24 July 2009 through 26 July 2009
ER -