Transaction item embedding by maximizing a joint probability

Yutaro Ueno, Masaomi Kimura

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2019 IEEE 4th International Conference on Computer and Communication Systems, ICCCS 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5-8
ページ数4
ISBN(電子版)9781728113227
DOI
出版ステータスPublished - 2019 2
イベント4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019 - Singapore, Singapore
継続期間: 2019 2 232019 2 25

出版物シリーズ

名前2019 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

ASJC Scopus subject areas

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

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