Adaptive Study Design Through Semantic Association Rule Analysis

Ping Chen; Wei Ding; Walter Garcia

doi:10.4018/978-1-4666-2651-5.ch010

Adaptive Study Design Through Semantic Association Rule Analysis

Ping Chen
, Wei Ding
, Walter Garcia

University of Massachusetts Boston

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experi ment shows promising results.

Original language	English
Title of host publication	Advances in Abstract Intelligence and Soft Computing
Publisher	IGI Global
Pages	132-144
Number of pages	13
ISBN (Electronic)	9781466626829
ISBN (Print)	9781466626515
DOIs	https://doi.org/10.4018/978-1-4666-2651-5.ch010
State	Published - Jan 1 2012

ASJC Scopus Subject Areas

General Computer Science
General Neuroscience
General Psychology

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Cite this

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AB - Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experi ment shows promising results.

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BT - Advances in Abstract Intelligence and Soft Computing

PB - IGI Global

ER -

Adaptive Study Design Through Semantic Association Rule Analysis

Abstract

ASJC Scopus Subject Areas

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