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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Outlier Detection Algorithms in Data Mining Systems
Programming and Computing Software
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
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
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernel Fisher Discriminants for Outlier Detection
Neural Computation
Outlier detection by sampling with accuracy guarantees
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection using the smallest kernel principal components
Outlier detection using the smallest kernel principal components
Outlier Detection with Kernel Density Functions
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ACM Computing Surveys (CSUR)
Kernel k-means for categorical data
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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Outlier detection is an important research topic that focuses on detecting abnormal information in data sets and processes. This paper addresses the problem of determining which class of kernels should be used in a geometric framework for nearest neighbor-based outlier detection. It introduces the class of similarity kernels and employs it within that framework. We also propose the use of isotropic stationary kernels for the case of normed input spaces. Two definitions of similarity scores using kernels are given: the k-NN kernel similarity score (kNNSS) and the summation kernel similarity score (SKSS). The paper concludes with preliminary experimental results comparing the performance of kNNSS and SKSS for outlier detection on four data sets. SKSS compared favorably to kNNSS.