Efficient frequent pattern mining from big data and its applications

Simple item page
dc.contributor.authorJiang, Fan
dc.contributor.examiningcommitteeGraham, Peter (Computer Science) Wang, Xikui (Statistics) Zaiane, Osmar (Computing Science, University of Alberta)en_US
dc.contributor.supervisorLeung, Carson (Computer Science)en_US
dc.date.accessioned2017-01-31T15:04:45Z
dc.date.available2017-01-31T15:04:45Z
dc.date.issued2014en_US
dc.date.issued2014en_US
dc.date.issued2015en_US
dc.date.issued2016en_US
dc.date.issued2016en_US
dc.date.issued2016en_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractFrequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide variety of valuable data of different veracities can be easily collected or generated at high velocity in various real-life applications. Among these 5V's of big data, I focus on handling high volumes of big data in my Ph.D. thesis. Specifically, I design and implement a new efficient frequent pattern mining algorithmic technique called B-mine, which overcomes some of the aforementioned drawbacks and achieves better performance when compared with existing algorithms. I also extend my B-mine algorithm into a family of algorithms that can perform big data mining efficiently. Moreover, I design four different frameworks that apply this family of algorithms to the real-life application of social network mining. Evaluation results show the efficiency and practicality of all these algorithms.en_US
dc.description.noteFebruary 2017en_US
dc.identifier.citationFan Jiang and Carson Kai-Sang Leung. Mining interesting "following" patterns from social networks. In Proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), Munich, Germany, pages 308-319. Springer, 2014.en_US
dc.identifier.citationFan Jiang and Carson Kai-Sang Leung. A business intelligence solution for frequent pattern mining on social networks. In Proceedings of the 2014 IEEE International Conference on Data Mining Workshops (ICDM Workshops 2014), Shenzhen, China, pages 789-796. IEEE, 2014.en_US
dc.identifier.citationCarson Kai-Sang Leung and Fan Jiang. Big data analytics of social networks for the discovery of "following" patterns. In Proceedings of the 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015), Valencia, Spain, pages 123-135. Springer, 2015.en_US
dc.identifier.citationCarson Kai-Sang Leung, Fan Jiang, Adam G. M. Pazdor, and Aaron M. Peddle. Parallel social network mining for interesting 'following' patterns. Concurrency and Computation: Practice and Experience, 28(15):3994-4012, 2016.en_US
dc.identifier.citationFan Jiang, Carson Kai-Sang Leung, and Hao Zhang. B-mine: Frequent pattern mining and its application to knowledge discovery from social networks. In Proceedings of the 18th Asia-Pacific Web Conference (APWeb 2016), Suzhou, China, pages 316-328. Springer, 2016.en_US
dc.identifier.citationEdson Dela Cruz, Carson Kai-Sang Leung, and Fan Jiang. Mining 'following' patterns from big sparse social networks. In Proceedings of the International Symposium on Foundations and Applications of Big Data Analytics (FAB 2016), San Francisco, CA, USA, pages 923-930. ACM, 2016.en_US
dc.identifier.urihttp://hdl.handle.net/1993/32083
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.publisherIEEEen_US
dc.publisherSpringeren_US
dc.publisherWileyen_US
dc.publisherSpringeren_US
dc.publisherACMen_US
dc.rightsopen accessen_US
dc.subjectFrequent Pattern Miningen_US
dc.subjectSocial Network Miningen_US
dc.titleEfficient frequent pattern mining from big data and its applicationsen_US
dc.typedoctoral thesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Jiang_Fan.pdf
Size:
706.62 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.2 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
FGS - Electronic Theses and Practica