Efficient frequent pattern mining from big data and its applications

Description

Frequent 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. / February 2017

Links & Downloads

1. Fan 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.
2. Fan 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.
3. Carson 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.
4. Carson 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.
5. Fan 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.
6. Edson 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.
7. http://hdl.handle.net/1993/32083

Tags

Frequent Pattern Mining

Social Network Mining

Additional Fields

Identifer	oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/32083
Date	January 2016
Creators	Jiang, Fan
Contributors	Leung, Carson (Computer Science), Graham, Peter (Computer Science) Wang, Xikui (Statistics) Zaiane, Osmar (Computing Science, University of Alberta)
Publisher	Springer, IEEE, Springer, Wiley, Springer, ACM
Source Sets	University of Manitoba Canada
Detected Language	English