Integrating many techniques for discovering structure in data

Dawn E. Gregory, Paul R. Cohen

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


This paper describes a formal representation of the discovery process that integrates of any number of data analysis strategies, regardless of their differences. We have implemented a system based on this formalization, called the Scientist’s Empirical Assistant (SEA). SEA employs several analysis strategies from the discovery literature, including techniques for function finding, causal modeling, and Bayesian conditioning. It uses high-level knowledge about the discovery process, the strategies, and the domain of study to coordinate the selection and application of analyses. It relies on the skills and initiatives of an expert user to guide its search for structure. Finally, it designs and runs experiments with a simulator to verify its findings. SEA’s primary sources of power are its abstraction of the discovery process and its numerous analysis strategies.

Original languageEnglish (US)
Title of host publicationAdvances in Intelligent Data Analysis
Subtitle of host publicationReasoning about Data - 2nd International Symposium, IDA-1997, Proceedings
EditorsXiaohui Liu, Paul Cohen, Michael Berthold
Number of pages12
ISBN (Print)9783540633464
StatePublished - 1997
Externally publishedYes
Event2nd International Symposium on Intelligent Data Analysis, IDA 1997 - London, United Kingdom
Duration: Aug 4 1997Aug 6 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Symposium on Intelligent Data Analysis, IDA 1997
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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