Learning partial-value variable associations

Nong Ye, Ting Yan Fok

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

Abstract

Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets all together in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations for different values of variables, and (3) unknown variable dependency. The Partial-Value Association Discovery (PVAD) algorithm handles these three characteristics of real-world data all together, and enables the discovery of partial-value and full-value variable associations as well as univariate and multivariate effects of variable values. This paper illustrates the PVAD algorithm using a small data set, and presents the new development of a computationally fast method to discover the longest associations with one conditional variable value or one associative variable value.

Original languageEnglish (US)
Title of host publicationICBDC 2019 - Proceedings of 2019 4th International Conference on Big Data and Computing
PublisherAssociation for Computing Machinery
Pages24-28
Number of pages5
ISBN (Electronic)9781450362788
DOIs
Publication statusPublished - May 10 2019
Event4th International Conference on Big Data and Computing, ICBDC 2019 - Guangzhou, China
Duration: May 10 2019May 12 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Big Data and Computing, ICBDC 2019
CountryChina
CityGuangzhou
Period5/10/195/12/19

    Fingerprint

Keywords

  • Data mining
  • Machine learning
  • Partial-value data association

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Ye, N., & Fok, T. Y. (2019). Learning partial-value variable associations. In ICBDC 2019 - Proceedings of 2019 4th International Conference on Big Data and Computing (pp. 24-28). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3335484.3335497