Robust regression and outlier detection
Robust regression and outlier detection
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Multidimensional binary search trees used for associative searching
Communications of the ACM
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Local peculiarity factor and its application in outlier detection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier Detection with Kernel Density Functions
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Local Outlier Detection Based on Kernel Regression
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Unsupervised ensemble learning for mining top-n outliers
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Outlier detection is an important and attractive problem in knowledge discovery in large data sets. The majority of the recent work in outlier detection follow the framework of Local Outlier Factor (LOF), which is based on the density estimate theory. However, LOF has two disadvantages that restrict its performance in outlier detection. First, the local density estimate of LOF is not accurate enough to detect outliers in the complex and large databases. Second, the performance of LOF depends on the parameter k that determines the scale of the local neighborhood. Our approach adopts the variable kernel density estimate to address the first disadvantage and the weighted neighborhood density estimate to improve the robustness to the variations of the parameter k, while keeping the same framework with LOF. Besides, we propose a novel kernel function named the Volcano kernel, which is more suitable for outlier detection. Experiments on several synthetic and real data sets demonstrate that our approach not only substantially increases the detection performance, but also is relatively scalable in large data sets in comparison to the state-of-the-art outlier detection methods.