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Volumn 1, Issue , 2009, Pages 415-418

An improved weighted-feature clustering algorithm for k-anonymity

Author keywords

Clustering; K anonymity; K partition

Indexed keywords

CATEGORICAL ATTRIBUTES; CLUSTERING QUALITY; FEATURE CLUSTERING; FEATURE WEIGHT; INFORMATION LOSS; K-ANONYMITY; K-PARTITION; MEMBER CLUSTERING; SUPPRESSION METHOD;

EID: 74049107643     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IAS.2009.311     Document Type: Conference Paper
Times cited : (7)

References (8)
  • 3
    • 26944448516 scopus 로고    scopus 로고
    • Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
    • J.Domingo-Ferrer, and V.Torra. "Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation", Data Mining and Knowledge Discovery, 11, 195-212, 2005.
    • (2005) Data Mining and Knowledge Discovery , vol.11 , pp. 195-212
    • Domingo-Ferrer, J.1    Torra, V.2
  • 7
    • 33646139547 scopus 로고    scopus 로고
    • Protecting respondent's privacy in microdata release
    • P. Samarati, "Protecting respondent's privacy in microdata release," TKDE, vol. 13, no. 6, 2001.
    • (2001) TKDE , vol.13 , Issue.6
    • Samarati, P.1
  • 8
    • 38049054142 scopus 로고    scopus 로고
    • Achieving k-anonymity via a density-based clustering method
    • H. Zhu and X. Ye, "Achieving k-anonymity via a density-based clustering method," in APWeb/WAIM, Lecture Notes in Computer Science 4505, pp. 745-752, 2007.
    • (2007) APWeb/WAIM, Lecture Notes in Computer Science , vol.4505 , pp. 745-752
    • Zhu, H.1    Ye, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.