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Volumn 29, Issue 2, 2000, Pages 93-104

LOF: Identifying density-based local outliers

Author keywords

Database mining; Outlier detection

Indexed keywords


EID: 0039253819     PISSN: 01635808     EISSN: None     Source Type: Journal    
DOI: 10.1145/335191.335388     Document Type: Article
Times cited : (6582)

References (23)
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    • Knorr, E.M.1    Ng, R.T.2
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    • Finding intensional knowledge of distance-based outliers
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    • Knorr E. M., Ng R. T.: "Finding Intensional Knowledge of Distance-based Outliers", Proc. 25th Int. Conf. on Very Large Data Bases, Edinburgh, Scotland, 1999, pp. 211-222.
    • (1999) Proc. 25th Int. Conf. on Very Large Data Bases , pp. 211-222
    • Knorr, E.M.1    Ng, R.T.2
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    • 0003136237 scopus 로고
    • Efficient and effective clustering methods for spatial data mining
    • Santiago, Chile, Morgan Kaufmann Publishers, San Francisco, CA
    • Ng R.T., Han J.: "Efficient and Effective Clustering Methods for Spatial Data Mining", Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile, Morgan Kaufmann Publishers, San Francisco, CA, 1994, pp. 144-155.
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    • Weber R., Schek Hans-J., Blott S.: "A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces", Proc. Int. Conf. on Very Large Data Bases, New York, NY, 1998, pp. 194-205.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.