The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Data Preparation for Mining World Wide Web Browsing Patterns
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Citations
Cites
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Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
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Efficient and Effective Clustering Methods for Spatial Data Mining
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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
Self-citationLetizia: an agent that assists web browsing
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Web Watcher: A Tour Guide for the World Wide Web.
Joachims, T., Freitag, D., & Mitchell, T. M. (1997) · Cited by 685
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Discovering Frequent Episodes in Sequences.
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1995) · Knowledge Discovery and Data Mining · Cited by 590
Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs
Zai͏̈ane, O. R., Xin, M., & Han, J. (2002) · IEEE International Forum on Research and Technology Advances in Digital Libraries (ADL) · doi:10.1109/adl.1998.670376 · Cited by 390
From user access patterns to dynamic hypertext linking
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Data mining for path traversal patterns in a web environment
Chen, M.-S., Park, J. S., & Yu, P. S. (2002) · Proceedings of 16th International Conference on Distributed Computing Systems · doi:10.1109/icdcs.1996.507986 · Cited by 327
Discovering generalized episodes using minimal occurrences
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Learning Information Retrieval Agents: Experiments with Automated Web Browsing
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In search of reliable usage data on the WWW
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Characterizing Browsing Behaviors on the World-Wide Web
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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
Self-citationEvaluation of session-based recommendation algorithms
Ludewig, M., & Jannach, D. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9209-6 · Cited by 336
Modeling consumer purchasing behavior in social shopping communities with clickstream data
Olbrich, R., & Holsing, C. (2011) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415160202 · Cited by 277
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
Self-citationExploring the potential of LMS log data as a proxy measure of student engagement
Henrie, C. R., Bodily, R., Larsen, R., & Graham, C. R. (2018) · Journal of Computing in Higher Education · doi:10.1007/s12528-017-9161-1 · Cited by 92
Metadata
- Authors
- Cooley, R., Mobasher, B., & Srivastava, J.
- Venue
- Knowledge and Information Systems
- Published
- 1999-02
- Volume
- 1
- Issue
- 1
- Pages
- 5-32
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- OpenAlex
- W1996869586
- Cited by
- 1,455
- References
- 22
- FWCI
- 142.60
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
