Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel Mining of Outliers in Large Database
Distributed and Parallel Databases
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Parallel Algorithms for Distance-Based and Density-Based Outliers
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Distance-Based Detection and Prediction of Outliers
IEEE Transactions on Knowledge and Data Engineering
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Data Mining and Knowledge Discovery
Mining distance-based outliers from large databases in any metric space
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
Algorithms for speeding up distance-based outlier detection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a distributed approach addressing the problem of distance-based outlier detection in very large data sets. The presented algorithm is based on the concept of outlier detection solving set ([1]), which is a small subset of the data set that can be provably used for predicting novel outliers. The algorithm exploits parallel computation in order to meet two basic needs: (i) the reduction of the run time with respect to the centralized version and (ii) the ability to deal with distributed data sets. The former goal is achieved by decomposing the overall computation into cooperating parallel tasks. Other than preserving the correctness of the result, the proposed schema exhibited excellent performances. As a matter of fact, experimental results showed that the run time scales up with respect to the number of nodes. The latter goal is accomplished through executing each of these parallel tasks only on a portion of the entire data set, so that the proposed algorithm is suitable to be used over distributed data sets. Importantly, while solving the distance-based outlier detection task in the distributed scenario, our method computes an outlier detection solving set of the overall data set of the same quality as that computed by the corresponding centralized method.