This work is licensed under the Creative Commons Attribution 4.0 International License.
Wen, F., Zhang, G., Sun, L., Wang, X., & Xu, X. (2019). A hybrid temporal association rules mining method for traffic congestion prediction. Computers & Industrial Engineering, vol. 130, pp.779–787.WenF.ZhangG.SunL.WangX.XuX.2019A hybrid temporal association rules mining method for traffic congestion predictionComputers & Industrial Engineering13077978710.1016/j.cie.2019.03.020Search in Google Scholar
Chen, C. H., Lan, G. C., Hong, T. P., & Lin, S. B. (2016). Mining fuzzy temporal association rules by item lifespans. Applied Soft Computing, vol. 41, pp. 265–274.ChenC. H.LanG. C.HongT. P.LinS. B.2016Mining fuzzy temporal association rules by item lifespansApplied Soft Computing4126527410.1016/j.asoc.2016.01.008Search in Google Scholar
Wang, C., & Zheng, X. (2019). Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint. Evolutionary Intelligence, vol. 132, pp.1–11.WangC.ZhengX.2019Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraintEvolutionary Intelligence13211110.1007/s12065-019-00234-5Search in Google Scholar
Sarma, H. K. D., & Mishra, S. (2016, December). Mining time series data with Apriori tid algorithm. In 2016 International Conference on Information Technology (ICIT) (pp. 160–164). IEEE.SarmaH. K. D.MishraS.2016DecemberMining time series data with Apriori tid algorithmIn2016 International Conference on Information Technology (ICIT)160164IEEE.10.1109/ICIT.2016.043Search in Google Scholar
Wang, L., Meng, J., Xu, P., & Peng, K. (2018). Mining temporal association rules with frequent itemsets tree. Applied Soft Computing, vol. 62, pp. 817–829.WangL.MengJ.XuP.PengK.2018Mining temporal association rules with frequent itemsets treeApplied Soft Computing6281782910.1016/j.asoc.2017.09.013Search in Google Scholar
Agarwal, R. (2018). Ordering policy and inventory classification using temporal association rule mining. International Journal of Productivity Management and Assessment Technologies (IJPMAT), vol.6, pp. 37–49.AgarwalR.2018Ordering policy and inventory classification using temporal association rule miningInternational Journal of Productivity Management and Assessment Technologies (IJPMAT)6374910.4018/IJPMAT.2018010103Search in Google Scholar
Nguyen, D. T., & Khuat, B. D. L. (2019). Discovery of Temporal Association Rules in Multivariate Time Series. International Journal of Applied Engineering Research, vol.14, pp.79–84.NguyenD. T.KhuatB. D. L.2019Discovery of Temporal Association Rules in Multivariate Time SeriesInternational Journal of Applied Engineering Research147984Search in Google Scholar
Li, Z., Bu, F., & Yu, F. (2017, July). Temporal fuzzy association rules mining based on fuzzy information granulation. In 2017 13th IEEE. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp.1168–1174.LiZ.BuF.YuF.2017JulyTemporal fuzzy association rules mining based on fuzzy information granulationIn2017 13th IEEE. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)1168117410.1109/FSKD.2017.8392930Search in Google Scholar
Zhou, H., & Hirasawa, K. (2019). Evolving temporal association rules in recommender system. Neural Computing and Applications, vol. 31, 2605–2619.ZhouH.HirasawaK.2019Evolving temporal association rules in recommender systemNeural Computing and Applications312605261910.1007/s00521-017-3217-zSearch in Google Scholar
Zhan, L., Yu, F., & Zhang, H. (2017, July). A fast algorithm for mining temporal association rules based on a new definition. In 2017 13th IEEE. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) pp.1548–1553.ZhanL.YuF.ZhangH.2017JulyA fast algorithm for mining temporal association rules based on a new definitionIn2017 13th IEEE. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)1548155310.1109/FSKD.2017.8392995Search in Google Scholar
Yonggang, W. (2016). Sequential association rules based on apriori algorithm applied in personal recommendation. International Journal of Database Theory and Application, vol.9, pp. 257–264.YonggangW.2016Sequential association rules based on apriori algorithm applied in personal recommendationInternational Journal of Database Theory and Application925726410.14257/ijdta.2016.9.6.26Search in Google Scholar
Radhakrishna, V., Kumar, P. V., & Janaki, V. (2016). An approach for mining similar temporal association patterns in single database scan. In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Vo. 2, pp. 607–617. Springer, Cham.RadhakrishnaV.KumarP. V.JanakiV.2016An approach for mining similar temporal association patterns in single database scanInProceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Vo. 2607617Springer, Cham.10.1007/978-3-319-30927-9_60Search in Google Scholar
Guo, Y., Wang, M. and Li, X. (2017), Application of an improved Apriori algorithm in a mobile e-commerce recommendation system, Industrial Management & Data Systems, Vol. 117, pp. 287–303.GuoY.WangM.LiX.2017Application of an improved Apriori algorithm in a mobile e-commerce recommendation systemIndustrial Management & Data Systems11728730310.1108/IMDS-03-2016-0094Search in Google Scholar
Krishna B., Amarawat G. (2019) Data Mining in Frequent Pattern Matching Using Improved Apriori Algorithm. In: Abraham A., Dutta P., Mandal J., Bhattacharya A., Dutta S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813, pp. 699–709. Springer, Singapore.KrishnaB.AmarawatG.2019Data Mining in Frequent Pattern Matching Using Improved Apriori AlgorithmIn:AbrahamA.DuttaP.MandalJ.BhattacharyaA.DuttaS.(eds)Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing813699709SpringerSingapore10.1007/978-981-13-1498-8_61Search in Google Scholar
Bhandari, A., Gupta, A., & Das, D. (2015). Improvised Apriori Algorithm Using Frequent Pattern Tree for Real Time Applications in Data Mining, Procedia Computer Science, vol. 45, pp. 644–651.BhandariA.GuptaA.DasD.2015Improvised Apriori Algorithm Using Frequent Pattern Tree for Real Time Applications in Data MiningProcedia Computer Science4564465110.1016/j.procs.2015.02.115Search in Google Scholar