TY - GEN
T1 - Clustering of web users' access patterns using a modified competitive agglomerative algorithm
AU - Veena, K. M.
AU - Pai, Radhika M.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/30
Y1 - 2017/11/30
N2 - Web recommendation systems are helpful in overcoming the excess information on web by retrieving the information required by the user with respect to user's or similar users' preferences and interests. In order to make web recommendation system work, web users have to be clustered based on their common interest. The web user clusters are used to obtain the knowledge about the web pages accessed. This knowledge can be used for prefetching of web pages, finding web pages that are frequently accessed together, etc. This paper presents a modification in the original CA clustering algorithm for grouping of web users with respect to access pattern of web pages. The original CA algorithm uses the basic "Fuzzy C Means (FCM) algorithm" to compute the membership matrix. The modified CA algorithm uses a superior FCM algorithm, namely, the Density Weighted FCM (DWFCM) instead of the basic FCM. Experiments are conducted on the datasets obtained from the UCI repository. It is found that the modified CA clustering method exhibits superior capability of clustering when compared to the CA clustering method.
AB - Web recommendation systems are helpful in overcoming the excess information on web by retrieving the information required by the user with respect to user's or similar users' preferences and interests. In order to make web recommendation system work, web users have to be clustered based on their common interest. The web user clusters are used to obtain the knowledge about the web pages accessed. This knowledge can be used for prefetching of web pages, finding web pages that are frequently accessed together, etc. This paper presents a modification in the original CA clustering algorithm for grouping of web users with respect to access pattern of web pages. The original CA algorithm uses the basic "Fuzzy C Means (FCM) algorithm" to compute the membership matrix. The modified CA algorithm uses a superior FCM algorithm, namely, the Density Weighted FCM (DWFCM) instead of the basic FCM. Experiments are conducted on the datasets obtained from the UCI repository. It is found that the modified CA clustering method exhibits superior capability of clustering when compared to the CA clustering method.
UR - https://www.scopus.com/pages/publications/85042930474
UR - https://www.scopus.com/pages/publications/85042930474#tab=citedBy
U2 - 10.1109/ICACCI.2017.8125924
DO - 10.1109/ICACCI.2017.8125924
M3 - Conference contribution
AN - SCOPUS:85042930474
VL - 2017-January
T3 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
SP - 701
EP - 707
BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Y2 - 13 September 2017 through 16 September 2017
ER -