The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Data mining for path traversal patterns in a web environment
Abstract
In this paper, we explore a new data mining capability which involved mining path traversal patterns in a distributed information providing environment like world-wide-web. First, we convert the original sequence of log data into a set of maximal forward references and filter out the effect of some backward references which are mainly made for ease of traveling. Second, we derive algorithms to determine the frequent traversal patterns, i.e., large reference sequences, from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences: one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed.
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Cited by
Web usage mining: discovery and applications of usage patterns from Web data
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.-N. (2000) · ACM SIGKDD Explorations Newsletter · doi:10.1145/846183.846188 · Cited by 2,105
Data Preparation for Mining World Wide Web Browsing Patterns
Cooley, R., Mobasher, B., & Srivastava, J. (1999) · Knowledge and Information Systems · doi:10.1007/bf03325089 · Cited by 1,455
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
Web mining: information and pattern discovery on the World Wide Web
Cooley, R., Mobasher, B., & Srivastava, J. (2002) · Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence · doi:10.1109/tai.1997.632303 · Cited by 1,162
Integrating Web Usage and Content Mining for More Effective Personalization
Mobasher, B., Dai, H., Luo, T., Sun, Y., & Zhu, J. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44463-7_15 · Cited by 259
Metadata
- Authors
- Chen, M.-S., Park, J. S., & Yu, P. S.
- Venue
- Proceedings of 16th International Conference on Distributed Computing Systems
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-23
- Pages
- 385-392
- OpenAlex
- W2149483835
- Cited by
- 327
- References
- 2
- FWCI
- 28.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, M.-S., Park, J. S., & Yu, P. S. (2002). Data mining for path traversal patterns in a web environment. Proceedings of 16th International Conference on Distributed Computing Systems. https://doi.org/10.1109/icdcs.1996.507986
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