SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The UCI KDD archive of large data sets for data mining research and experimentation
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Learning nonstationary models of normal network traffic for detecting novel attacks
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Very efficient mining of distance-based outliers
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Efficient Pruning Schemes for Distance-Based Outlier Detection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Distance-Based outlier detection on uncertain data of gaussian distribution
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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Statistical Analysis and Data Mining
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Data Mining and Knowledge Discovery
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Detecting outliers in data is an important problem with interesting applications in a myriad of domains ranging from data cleaning to financial fraud detection and from network intrusion detection to clinical diagnosis of diseases. Over the last decade of research, distance-based outlier detection algorithms have emerged as a viable, scalable, parameter-free alternative to the more traditional statistical approaches. In this paper we assess several distance-based outlier detection approaches and evaluate them. We begin by surveying and examining the design landscape of extant approaches, while identifying key design decisions of such approaches. We then implement an outlier detection framework and conduct a factorial design experiment to understand the pros and cons of various optimizations proposed by us as well as those proposed in the literature, both independently and in conjunction with one another, on a diverse set of real-life datasets. To the best of our knowledge this is the first such study in the literature. The outcome of this study is a family of state of the art distance-based outlier detection algorithms. Our detailed empirical study supports the following observations. The combination of optimization strategies enables significant efficiency gains. Our factorial design study highlights the important fact that no single optimization or combination of optimizations (factors) always dominates on all types of data. Our study also allows us to characterize when a certain combination of optimizations is likely to prevail and helps provide interesting and useful insights for moving forward in this domain.