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Volumn , Issue , 2010, Pages 501-505

Using fuzzy ant colony optimization for diagnosis of diabetes disease

Author keywords

Ant colony optimization; Classification; Diabetes diagnosis; Fuzzy logic; Medical data mining

Indexed keywords

ANT COLONY OPTIMIZATION; CLASSIFICATION; CLASSIFICATION SYSTEM; DATA SETS; FUZZY IF-THEN RULES; MEDICAL DATA MINING; PIMA INDIAN DIABETES; RULE-BASED CLASSIFICATION SYSTEM; SET OF RULES;

EID: 77954962173     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IRANIANCEE.2010.5507019     Document Type: Conference Paper
Times cited : (48)

References (17)
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    • (2008) Expert Systems with Applications , vol.34 , pp. 482-487
    • Polat, K.1    Günes, S.2    Arslan, A.3
  • 4
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    • (2009) Expert Systems with Applications , vol.36 , pp. 8610-8615
    • Temurtas, H.1    Yumusak, N.2    Temurtas, F.3
  • 6
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    • (2005) Theoretical Computer Science , vol.344 , pp. 243-278
    • Dorigo, M.1    Blum, C.2
  • 8
    • 53749108581 scopus 로고    scopus 로고
    • Finding groups in data: Cluster analysis with ants
    • Urszula Boryczka, Finding groups in data: Cluster analysis with ants, Applied Soft Computing, Vol. 9, pp.61-70, 2009.
    • (2009) Applied Soft Computing , vol.9 , pp. 61-70
    • Boryczka, U.1
  • 9
    • 28944454561 scopus 로고    scopus 로고
    • Review Ant colony optimization: Introduction and recent trends
    • Christian Blum, Review Ant colony optimization: Introduction and recent trends, Physics of Life Reviews Vol.2, pp. 353-373, 2005.
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    • Blum, C.1
  • 11
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    • last accessed: November 2009
    • American diabetes association. http://www.diabetes.org/diabetes-basics (last accessed: November 2009)


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.