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

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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.

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Acknowledgments

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 20152062051 and NRF No. 20155054624).

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Authors and Affiliations

  1. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Gangin Lee & Unil Yun

  2. Texas A&M AgriLife Research, Texas A&M University System, College Station, TX, 77841, USA

    Kyung-Min Lee

Authors
  1. Gangin Lee
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  2. Unil Yun
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  3. Kyung-Min Lee
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Correspondence to Unil Yun.

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Lee, G., Yun, U. & Lee, KM. Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses. J Supercomput 72, 4296–4318 (2016). https://doi.org/10.1007/s11227-016-1847-z

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