The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Frequent pattern mining: current status and future directions
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Citations
Cites
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Self-citationAgrawal, R., & Srikant, R. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380415 · Cited by 5,155
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335372 · Cited by 3,216
Self-citationMining sequential patterns: Generalizations and performance improvements
Srikant, R., & Agrawal, R. (1996) · Lecture Notes in Computer Science · doi:10.1007/bfb0014140 · Cited by 2,711
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
Integrating classification and association rule mining
Liu, B., Hsu, W., & Ma, Y. (1998) · Cited by 2,247
Cited by
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620
Occupancy schedules learning process through a data mining framework
D’Oca, S., & Hong, T. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2014.11.065 · Cited by 294
Digital Twin of City: Concept Overview
Ivanov, S., Nikolskaya, K., Radchenko, G., Sokolinsky, L., & Zymbler, M. (2020) · 2020 Global Smart Industry Conference (GloSIC) · doi:10.1109/glosic50886.2020.9267879 · Cited by 138
Integrating induction and deduction for finding evidence of discrimination
Pedreschi, D., Ruggieri, S., & Turini, F. (2009) · Proceedings of the 12th International Conference on Artificial Intelligence and Law · doi:10.1145/1568234.1568252 · Cited by 55
Lin, W., Ma, M., Pan, D., & Wang, P. (2018) · 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) · doi:10.1109/pccc.2018.8711092 · Cited by 29
Metadata
- Authors
- Han, J., Cheng, H., Xin, D., & Yan, X.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2007-01-27
- Volume
- 15
- Issue
- 1
- Pages
- 55-86
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2110893883
- Cited by
- 1,380
- References
- 136
- FWCI
- 165.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-006-0059-1
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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.
