Functional behavioral assessment using the LERS data mining system - Strategies for understanding complex physiological and behavioral patterns

Rachel L Freeman, Jerzy W. Grzymala-Busse, Mark Harvey

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Many individuals with mental retardation, autism, and other related disabilities lead lives that are significantly restricted because of problem behaviors such as self-injury and aggression. We processed two data sets, one describing heart rate patterns and the other describing the behavioral events of one subject diagnosed with severe mental retardation, visual impairments, and severe problem behavior. From these data sets the LERS data mining system induced certain and possible rule sets. In our research these rule sets were successfully used for interpretation, or, more specifically, to discover mechanisms of triggering specific physiological and behavioral patterns.

Original languageEnglish (US)
Pages (from-to)173-181
Number of pages9
JournalJournal of Intelligent Information Systems
Volume21
Issue number2
DOIs
StatePublished - Sep 1 2003

Keywords

  • Data mining
  • LEM2 algorithm
  • LERS
  • Self-injurious behavior

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