TY - GEN
T1 - Mining access patterns efficiently from web logs
AU - Pei, Jian
AU - Han, Jiawei
AU - Mortazavi-Asl, Behzad
AU - Zhu, Hua
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2000.
PY - 2000
Y1 - 2000
N2 - With the explosive growth of data avaiilable on the World Wide Web, discovery and analysis of useful information from the World Wide Web becomes a practical necessity. Web access pattern, which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice. In this paper, we study the problem of mining access patterns from Web logs efficiently. A novel data structure, called Web access pattern tree, or WAP-tree in short, is developed for efficient mining of access patterns from pieces of logs. The Web access pattern tree stores highly compressed, critical information for access pattern mining and facilitates the development of novel algorithms for mining access patterns in large set of log pieces. Our algorithm can find access patterns from Web logs quite efficiently. The experimental and performance studies show that our method is in general an order of magnitude faster than conventional methods.
AB - With the explosive growth of data avaiilable on the World Wide Web, discovery and analysis of useful information from the World Wide Web becomes a practical necessity. Web access pattern, which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice. In this paper, we study the problem of mining access patterns from Web logs efficiently. A novel data structure, called Web access pattern tree, or WAP-tree in short, is developed for efficient mining of access patterns from pieces of logs. The Web access pattern tree stores highly compressed, critical information for access pattern mining and facilitates the development of novel algorithms for mining access patterns in large set of log pieces. Our algorithm can find access patterns from Web logs quite efficiently. The experimental and performance studies show that our method is in general an order of magnitude faster than conventional methods.
UR - http://www.scopus.com/inward/record.url?scp=77957967271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957967271&partnerID=8YFLogxK
U2 - 10.1007/3-540-45571-x_47
DO - 10.1007/3-540-45571-x_47
M3 - Conference contribution
AN - SCOPUS:77957967271
SN - 3540673822
SN - 9783540673828
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 396
EP - 407
BT - Knowledge Discovery and Data Mining
A2 - Terano, Takao
A2 - Liu, Huan
A2 - Chen, Arbee L.P.
PB - Springer
T2 - 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
Y2 - 18 April 2000 through 20 April 2000
ER -