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AuthorZhang, Haiqing
AuthorLi, Daiwei
AuthorLi, Tianrui
AuthorYu, Xi
AuthorTao, Wang
AuthorBouras, Abdelaziz
Available date2021-01-25T06:45:46Z
Publication Date2017
Publication Name2017 2nd International Conference on Image, Vision and Computing, ICIVC 2017
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICIVC.2017.7984541
URIhttp://hdl.handle.net/10576/17442
AbstractAdvanced pattern mining to extract the hidden but useful information by using proper structure is vital important for efficient information mining in large-scale practical datasets. The existing algorithms have not been capable of effective solving the fuzziness uncertainty of items and confirming the appropriate structure of studied patterns. In order to generate more proper practical patterns, a base-(second-order-effect) pattern structure is proposed to represent the internal relationships among items. In addition, fuzzy weight constraints and properties have been presented to reflect the importance of uncertainty for each item in a whole dataset and in one transaction. Thus, the proposed maximal FSFPs mining algorithm guarantees efficient mining performance based on the proposed advanced pattern-aware dynamic search strategy, preventing overheads of pattern extraction based on the pruning strategies, and adopting fuzzy weight conditions to enhance the dependability of mining results. The extensive experimental results obtained from six benchmark datasets indicate that our algorithm has outstanding performance in comparison to PADS and FPMax? algorithms.
SponsorThis research was supported by the National Natural Science Foundation of China (NSFC) (No. 61602064, No.61502059), Scientific Research Foundation of CUlT (No. KYTZ201615), Science and Technology of Sichuan Province (No. 2017HH0088), and Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province (Grant No. MSSB-2016-02).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectFSFP-tree
Fuzzy weight constraints
Maximal fuzzy supplement frequent pattern
Pattern-aware dynamic approach
TitleA pattern-aware method for maximal fuzzy supplement frequent pattern mining
TypeConference Paper
Pagination173-179
dc.accessType Abstract Only


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