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SIGMOD
2000
ACM
173views Database» more  SIGMOD 2000»
10 years 1 months ago
Efficient Algorithms for Mining Outliers from Large Data Sets
In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis o...
Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim
KDD
2006
ACM
142views Data Mining» more  KDD 2006»
10 years 10 months ago
Mining distance-based outliers from large databases in any metric space
Let R be a set of objects. An object o R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric ar...
Yufei Tao, Xiaokui Xiao, Shuigeng Zhou
KDD
2005
ACM
205views Data Mining» more  KDD 2005»
10 years 3 months ago
Feature bagging for outlier detection
Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel feature bagging approach for detecting outliers in...
Aleksandar Lazarevic, Vipin Kumar
VLDB
1998
ACM
192views Database» more  VLDB 1998»
10 years 1 months ago
Algorithms for Mining Distance-Based Outliers in Large Datasets
This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in ...
Edwin M. Knorr, Raymond T. Ng
SIGMOD
1998
ACM
99views Database» more  SIGMOD 1998»
10 years 1 months ago
CURE: An Efficient Clustering Algorithm for Large Databases
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
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