NDLI: Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses

Content Provider	Springer Nature Link
Author	Lee, Gangin Yun, Unil Lee, Kyung Min
Copyright Year	2016
Abstract	Various large-scale data have been generated in a variety of application fields, since the Internet began to be widely used. Accordingly, researchers have developed various data mining methods for pervasive human-centric computing to deal with the data and discover interesting knowledge. Frequent pattern mining is one of the main issues in data mining, which finds meaningful pattern information from databases. In this area, not only precise data but also uncertain data can be generated depending on environments of data generation. Since the concept of uncertain frequent pattern mining was proposed to overcome the limitations of traditional approaches that cannot deal with uncertain data with existential probabilities of items, several relevant methods have been developed. In this paper, we introduce and analyze state-of-the-art methods based on tree structures, and propose a new uncertain frequent pattern mining approach. We also compare algorithm performance and discuss characteristics of them.
Starting Page	4296
Ending Page	4318
Page Count	23
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	72
Issue Number	11
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2016-08-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Frequent pattern mining Performance evaluation Uncertain data Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture Software

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