Reframing in Frequent Pattern Mining

Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
285 Downloads (Pure)


Mining frequent patterns is a crucial task in data mining. Most of the existing frequent pattern mining methods find the complete set of frequent patterns from a given dataset. However, in real-life scenarios we often need to predict the future frequent patterns for different tasks such as business policy making, web page recommendation, stock-market behavior and road traffic analysis. Predicting future frequent patterns from the currently available set of frequent patterns is challenging due to dataset shift where data distributions may change from one dataset to another. In this paper, we propose a new approach called reframing in frequent pattern mining to solve this task. Moreover, we experimentally show the existence of dataset shift in two real-life transactional datasets and the capability of our approach to handle these unknown shifts.
Original languageEnglish
Title of host publication2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015)
Subtitle of host publicationProceedings of a meeting held 9-11 November 2015 at Vietri sul Mare, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781509001637
ISBN (Print)9781509001644
Publication statusPublished - Apr 2016

Publication series

NameProceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1082-3409

Structured keywords

  • Jean Golding


  • Data Mining
  • Frequent Pattern Mining
  • Dataset Shift
  • Machine Learning
  • Adaptation

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