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Trans2Vec: Learning Transaction Embedding via Items and Frequent Itemsets

  • Dang Nguyen
  • Tu Dinh Nguyen
  • Wei Luo
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

Learning meaningful and effective representations for transaction data is a crucial prerequisite for transaction classification and clustering tasks. Traditional methods which use frequent itemsets (FIs) as features often suffer from the data sparsity and high-dimensionality problems. Several supervised methods based on discriminative FIs have been proposed to address these disadvantages, but they require transaction labels, thus rendering them inapplicable to real-world applications where labels are not given. In this paper, we propose an unsupervised method which learns low-dimensional continuous vectors for transactions based on information of both singleton items and FIs. We demonstrate the superior performance of our proposed method in classifying transactions on four datasets compared with several state-of-the-art baselines.

Notes

Acknowledgment

This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning. Tu Dinh Nguyen gratefully acknowledges the partial support from the Australian Research Council (ARC).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dang Nguyen
    • 1
  • Tu Dinh Nguyen
    • 1
  • Wei Luo
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data Analytics, School of Information TechnologyDeakin UniversityGeelongAustralia

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