Machine Learning
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
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Discovering cluster-based local outliers
Pattern Recognition Letters
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness, Peculiarity, and Multi-database Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Relational Peculiarity Oriented 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
Rule + Exception Strategies for Security Information Analysis
IEEE Intelligent Systems
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing rank-inducing scoring systems
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Relational peculiarity-oriented mining
Data Mining and Knowledge Discovery
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
RKOF: robust kernel-based local outlier detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
A survey of outlier detection methodologies and their applications
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
ExpertRec: a collaborative web search engine
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
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
Subsampling for efficient and effective unsupervised outlier detection ensembles
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification and outlier detection based on topic based pattern synthesis
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Peculiarity oriented mining (POM), aiming to discover peculiarity rules hidden in a dataset, is a new data mining method. In the past few years, many results and applications on POM have been reported. However, there is still a lack of theoretical analysis. In this paper, we prove that the peculiarity factor (PF), one of the most important concepts in POM, can accurately characterize the peculiarity of data with respect to the probability density function of a normal distribution, but is unsuitable for more general distributions. Thus, we propose the concept of local peculiarity factor (LPF). It is proved that the LPF has the same ability as the PF for a normal distribution and is the so-called µ-sensitive peculiarity description for general distributions. To demonstrate the effectiveness of the LPF, we apply it to outlier detection problems and give a new outlier detection algorithm called LPF-Outlier. Experimental results show that LPF-Outlier is an effective outlier detection algorithm.