Applying data-mining techniques for discovering association rules

Mu-Jung Huang, Hsiu Shu Sung, Tsu Jen Hsieh, Ming-Cheng Wu, Shao Hsi Chung

Research output: Contribution to journalArticle

Abstract

Data mining has become a hot research topic, and how to mine valuable knowledge from such huge volumes of data remains an open problem. Processing huge volumes of data presents a challenge to existing computation software and hardware. This study proposes a model using association rule mining (ARM) which is a kind of data-mining technique for discovering association rules of chronic diseases from the enormous data that are collected continuously through health examination and medical treatment. This study makes three critical contributions: (1) It suggests a systematical model of exploring huge volumes of data using ARM, (2) it shows that helpful implicit rules are discovered through data-mining techniques, and (3) the results proved that the proposed model can act as an expert system for discovering useful knowledge from huge volumes of data for the references of doctors and patients to the specific chronic diseases prognosis and treatments.

Original languageEnglish
JournalSoft Computing
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Association rules
Association Rules
Data mining
Data Mining
Chronic Disease
Association Rule Mining
Expert systems
Data Association
Prognosis
Expert System
Health
Hardware
Open Problems
Model
Processing
Software
Knowledge

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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Applying data-mining techniques for discovering association rules. / Huang, Mu-Jung; Sung, Hsiu Shu; Hsieh, Tsu Jen; Wu, Ming-Cheng; Chung, Shao Hsi.

In: Soft Computing, 01.01.2019.

Research output: Contribution to journalArticle

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