1 |
KIRAN R U, SHANG H C, TOYODA M, et al. Discovering partial periodic itemsets in temporal databases[C]// Proceedings of the 29th International Conference on Scientific and Statistical Database Management. New York: ACM, 2017: No.30. 10.1145/3085504.3085535
|
2 |
LIU Z S, MA Y M, ZHENG H H, et al. Human resource recommendation algorithm based on improved frequent itemset mining[J]. Future Generation Computer Systems, 2022, 126: 284-288. 10.1016/j.future.2021.08.017
|
3 |
MIRBAGHERI S M, HAMILTON H J. Mining high utility patterns in interval-based event sequences[J]. Data and Knowledge Engineering, 2021, 135: No.101924. 10.1016/j.datak.2021.101924
|
4 |
WU Y X, LEI R, LI Y, et al. HAOP-Miner: self-adaptive high-average utility one-off sequential pattern mining[J]. Expert Systems with Applications, 2021, 184: No.115449. 10.1016/j.eswa.2021.115449
|
5 |
TANBEER S K, AHMED C F, JEONG B S, et al. Discovering periodic-frequent patterns in transactional databases[C]// Proceedings of the 2009 Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 5476. Berlin: Springer, 2009: 242-253.
|
6 |
SHELENKOV A, KOROTKOV E. LEPSCAN — a web server for searching latent periodicity in DNA sequences[J]. Briefings in Bioinformatics, 2012, 13(2): 143-149. 10.1093/bib/bbr044
|
7 |
KIRAN R U, KITSUREGAWA M, REDDY P K. Efficient discovery of periodic-frequent patterns in very large databases[J]. Journal of Systems and Software, 2016, 112: 110-121. 10.1016/j.jss.2015.10.035
|
8 |
FOURNIER-VIGER P, LIN C W, DUONG Q H, et al. PFPM: discovering periodic frequent patterns with novel periodicity measures[C]// Proceedings of the 2nd Czech-China Scientific Conference. London: IntechOpen, 2017: 23-35. 10.5772/66780
|
9 |
KIRAN R U, KITSUREGAWA M. Discovering quasi-periodic-frequent patterns in transactional databases[C]// Proceedings of the 2013 International Conference on Big Data Analytics, LNCS 8302. Cham: Springer, 2013: 97-115.
|
10 |
KIRAN R U, VENKATESH J N, FOURNIER-VIGER P, et al. Discovering periodic patterns in non-uniform temporal databases[C]// Proceedings of the 2017 Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 10235. Cham: Springer, 2017: 604-617.
|
11 |
SAIDEEP C, KIRAN R U, ZETTSU K, et al. Parallel mining of partial periodic itemsets in big data[C]// Proceedings of the 2020 International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, LNCS 12144. Cham: Springer, 2020: 807-819.
|
12 |
LIKHITHA P, RAVIKUMAR P, KIRAN R U, et al. Discovering closed periodic-frequent patterns in very large temporal databases[C]// Proceedings of the 2020 IEEE International Conference on Big Data. Piscataway: IEEE, 2020: 4700-4709. 10.1109/bigdata50022.2020.9378215
|
13 |
ARYABARZAN N, MINAEI-BIDGOLI B. NEclatClosed: a vertical algorithm for mining frequent closed itemsets[J]. Expert Systems with Applications, 2021, 174: No.114738. 10.1016/j.eswa.2021.114738
|
14 |
CAI S H, HUANG R B, CHEN J F, et al. An efficient outlier detection method for data streams based on closed frequent patterns by considering anti-monotonic constraints[J]. Information Sciences, 2021, 555: 125-146. 10.1016/j.ins.2020.12.050
|
15 |
HACKMAN A, HUANG Y, TSENG V S. Mining trending high utility itemsets from temporal transaction databases[C]// Proceedings of the 2018 International Conference on Database and Expert Systems Applications, LNCS 11030. Cham: Springer, 2018: 461-470.
|
16 |
KANG R, WANG C, WANG P, et al. Matching consecutive subpatterns over streaming time series[C]// Proceedings of the 2018 Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, LNCS 10988. Cham: Springer, 2018: 90-105.
|
17 |
UPADHYAY P, PANDEY M K, KOHLI N. Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified FP tree[J]. Soft Computing, 2021, 25(6): 4327-4344. 10.1007/s00500-020-05444-z
|
18 |
XUN Y L, CUI X H, ZHANG J F, et al. Incremental frequent itemsets mining based on frequent pattern tree and multi-scale[J]. Expert Systems with Applications, 2021, 163: No.113805. 10.1016/j.eswa.2020.113805
|
19 |
JINDAL R, BORAH M D. A novel approach for mining frequent patterns from incremental data[J]. International Journal of Data Mining, Modelling and Management, 2016, 8(3): 244-264. 10.1504/ijdmmm.2016.079071
|
20 |
HUYNH V Q P, KÜNG J, DANG T K. Incremental frequent itemsets mining with IPPC tree[C]// Proceedings of the 2017 International Conference on Database and Expert Systems Applications, LNCS 10438. Cham: Springer, 2017: 463-477.
|
21 |
BEAN B, SUN Y, MAGUIRE M. Interval-valued kriging for geostatistical mapping with imprecise inputs[J]. International Journal of Approximate Reasoning, 2022, 140: 31-51. 10.1016/j.ijar.2021.10.003
|
22 |
NAKAMURA S, KIRAN R U, LIKHITHA P, et al. Efficient discovery of partial periodic-frequent patterns in temporal databases[C]// Proceedings of the 2021 International Conference on Database and Expert Systems Applications, LNCS 12923. Cham: Springer, 2021: 221-227.
|