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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1992) · Cited by 23,431
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
A Extensible Markup Language XML
(2009) · Foundations of Semantic Web Technologies · doi:10.1201/9781420090512-18 · Cited by 4,824
Mining sequential patterns: Generalizations and performance improvements
Srikant, R., & Agrawal, R. (1996) · Lecture Notes in Computer Science · doi:10.1007/bfb0014140 · Cited by 2,711
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., & Pirahesh, H. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009726021843 · Cited by 2,134
Efficient and Effective Clustering Methods for Spatial Data Mining
Ng, R. T., & Han, J. (1994) · Cited by 1,778
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-citation
Cited by
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
Exploring 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
(2011) · The Electronic Journal of e-Learning · Cited by 24
Activity Frames: Deterministic Screen-Activity Compilation for Agent Memory and Replay
Iyamu, N. (2026) · arXiv
“…critique that unstructured total capture serves no-body [26] is the failure mode we exist to prevent, plus web sessionization’s inactivity timeouts [27], process-mining event abstraction [28], robotic process mining’s UI-log routine identification [29], and the data-to-text faithfulness tradition…”
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Data Preparation for Mining World Wide Web Browsing Patterns. https://app.cochat.ai/papers/10.1007/bf03325089
- The paper
- Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems. https://doi.org/10.1007/bf03325089
Data sources
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.
