The Partial-Value Association Discovery Algorithm to Learn Multilayer Structural System Models from System Data

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Many systems exist without us knowing structural system models and require us to discover structural system models from system data. A major shortcoming of current statistical modeling and data mining techniques is their focus on building relations of variables that hold for all values of variables. This paper presents the new partial-value association discovery (PVAD) algorithm to discover relations of variables that may exist for only certain values or different value ranges of variables and to use these partial-value variable relations for constructing structural system models. The PVAD algorithm along with its performance and computational complexity is presented.

Original languageEnglish (US)
Article number7517346
Pages (from-to)3377-3385
Number of pages9
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume47
Issue number12
DOIs
StatePublished - Dec 1 2017

Keywords

  • Categorical and numeric data
  • data mining
  • partial-value associations of variable values
  • structural system model

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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