Trans2Vec: learning transaction embedding via items and frequent itemsets

Dang Nguyen, Tu Dinh Nguyen, Wei Luo, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication22nd Pacific-Asia Conference, PAKDD 2018 Melbourne, VIC, Australia, June 3–6, 2018 Proceedings, Part III
EditorsDinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783319930404
ISBN (Print)9783319930398
Publication statusPublished - 2018
Externally publishedYes
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2018 - Grand Hyatt, Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018
Conference number: 22nd

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2018
Abbreviated titlePAKDD 2018
Internet address

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