Development of a new metric to identify rare patterns in association analysis: The case of analyzing diabetes complications
Authors:
- Saeed PiriSPSaeed Piri
- Dursun DelenDDDursun Delen
- Tieming LiuTLTieming Liu
- William PaivaWPWilliam Paiva
- 1 March 2018
- journal article
- research article
- Published by Elsevier in Expert Systems with Applications
- Vol. 94, 112-125
Abstract
No abstract availableKeywords
Funding Information
- Oklahoma State University
This publication has 27 references indexed in Scilit:
- Unsupervised Rare Pattern MiningACM Transactions on Knowledge Discovery From Data, 2016
- A primer to frequent itemset mining for bioinformaticsBriefings in Bioinformatics, 2013
- Health Disparities in Endocrine Disorders: Biological, Clinical, and Nonclinical Factors—An Endocrine Society Scientific StatementThe Journal of Clinical Endocrinology & Metabolism, 2012
- Comorbidity Study on Type 2 Diabetes Mellitus Using Data MiningThe Korean Journal of Internal Medicine, 2012
- Impact of Comorbidity on Survival Among Men With Localized Prostate CancerJournal of Clinical Oncology, 2011
- Longitudinal Patterns in Survival, Comorbidity, Healthcare Utilization and Quality of Care among Older Women Following Breast Cancer DiagnosisJournal of General Internal Medicine, 2010
- Survival disparities in Indigenous and non-Indigenous New Zealanders with colon cancer: the role of patient comorbidity, treatment and health service factorsJournal of Epidemiology and Community Health, 2010
- Ethnic Differences in Mortality, End-Stage Complications, and Quality of Care Among Diabetic PatientsDiabetes Care, 2005
- Alternative interest measures for mining associations in databasesIEEE Transactions on Knowledge and Data Engineering, 2003
- Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE dataGenome Biology, 2002