Transaction item embedding by maximizing a joint probability

Yutaro Ueno, Masaomi Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Frequent pattern mining plays an important role in the data mining field. This topic has been studied for a long time. Most of the method which finds frequent pattern mining is high computational cost. In general, transaction data is sparse. Therefore, searching frequent itemsets in a dense part of transaction data is better than searching all transaction data. In this paper, we propose a method to embed items from transactions to a low dimensional vector space. We show the relationship between transaction data and a low dimensional vector space which is created by our method.

Original languageEnglish
Title of host publication2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-8
Number of pages4
ISBN (Electronic)9781728113227
DOIs
Publication statusPublished - 2019 Feb
Event4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 - Singapore, Singapore
Duration: 2019 Feb 232019 Feb 25

Publication series

Name2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019

Conference

Conference4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019
CountrySingapore
CitySingapore
Period19/2/2319/2/25

Keywords

  • Data mining
  • F requent pattern mining
  • Itemset mining
  • Machine learning
  • Transaction dataset

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems

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  • Cite this

    Ueno, Y., & Kimura, M. (2019). Transaction item embedding by maximizing a joint probability. In 2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019 (pp. 5-8). [8821745] (2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCOMS.2019.8821745