Computational geometry: an introduction
Computational geometry: an introduction
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Computing depth contours of bivariate point clouds
Computational Statistics & Data Analysis - Special issue on classification
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
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
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third 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
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
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
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Outlier Detection Based on Voronoi Diagram
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
ACM Computing Surveys (CSUR)
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Review: A review of novelty detection
Signal Processing
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Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets.