PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Genomics & Informatics10.5808/gi.2012.10.1.51201210151An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence DatabasesMd. Rezaul Karim, Md. Mamunur Rashid, Byeong-Soo Jeong, Ho-Jin Choihttp://synapse.koreamed.org/DOIx.php?id=10.5808/GI.2012.10.1.51
Journal of Intelligent Systems10.1515/jisys-2014-00402015242181-197Efficient Algorithms for Mining Frequent Patterns from Sparse and Dense DatabasesLan Vu, Gita Alaghbandhttps://www.degruyter.com/view/journals/jisys/24/2/article-p181.xml, https://www.degruyter.com/document/doi/10.1515/jisys-2014-0040/pdf
Concurrency and Computation: Practice and Experience10.1002/cpe.52432019333Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphsK Lakshmi, T Meyyappanhttps://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fcpe.5243, https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5243, https://onlinelibrary.wiley.com/doi/full-xml/10.1002/cpe.5243, https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5243
Information Sciences10.1016/j.ins.2017.11.0642018432278-300Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approachMd. Rezaul Karim, Michael Cochez, Oya Deniz Beyan, Chowdhury Farhan Ahmed, Stefan Deckerhttps://api.elsevier.com/content/article/PII:S002002551731126X?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S002002551731126X?httpAccept=text/plain
Knowledge and Information Systems10.1007/s10115-007-0115-12007163359-391Efficient mining of maximal frequent itemsets from databases on a cluster of workstationsSoon M. Chung, Congnan Luohttp://link.springer.com/content/pdf/10.1007/s10115-007-0115-1.pdf, http://link.springer.com/article/10.1007/s10115-007-0115-1/fulltext.html, http://link.springer.com/content/pdf/10.1007/s10115-007-0115-1
Periodic Pattern Mining10.1007/978-981-16-3964-7_2202123-40Discovering Frequent Patterns in Very Large Transactional DatabasesJose M. Lunahttps://link.springer.com/content/pdf/10.1007/978-981-16-3964-7_2
Lecture Notes in Computer Science10.1007/978-3-642-28320-8_222012254-266An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional DatabasesAkshat Surana, R. Uday Kiran, P. Krishna Reddyhttp://link.springer.com/content/pdf/10.1007/978-3-642-28320-8_22
Knowledge-Based Systems10.1016/j.knosys.2012.02.00220123353-64An efficient mining algorithm for maximal weighted frequent patterns in transactional databasesUnil Yun, Hyeonil Shin, Keun Ho Ryu, EunChul Yoonhttps://api.elsevier.com/content/article/PII:S0950705112000366?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0950705112000366?httpAccept=text/plain
2021 2nd International Conference for Emerging Technology (INCET)10.1109/incet51464.2021.94561772021An Efficient Approach for Mining Weighted Frequent Patterns from Data StreamJesan Ahammed Ovi, Md Ashraful Islam, Jannatul Ferdosh Nimahttp://xplorestaging.ieee.org/ielx7/9456097/9456045/09456177.pdf?arnumber=9456177
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/dsaa49011.2020.000132020Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal DatabasesR. Uday Kiran, Yutaka Watanobe, Bhaskar Chaudhury, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawahttp://xplorestaging.ieee.org/ielx7/9259989/9259990/09260063.pdf?arnumber=9260063