Effective clustering of complex objects in object-oriented databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
Predicting rare classes: can boosting make any weak learner strong?
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting Rare Events In Temporal Domains
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Building the Data Warehouse
Mining most general multidimensional summarization of probably groups in data warehouses
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
ACM Computing Surveys (CSUR)
A comprehensive survey of numeric and symbolic outlier mining techniques
Intelligent Data Analysis
Clustering Uncertain Data Based on Probability Distribution Similarity
IEEE Transactions on Knowledge and Data Engineering
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Relationship management is critical in business. Particularly, it is important to detect abnormal relationships, such as fraudulent relationships between service providers and consumers. Surprisingly, in the literature there is no systematic study on detecting relationship outliers. Particularly, no existing methods can detect and handle relationship outliers between groups and individuals in groups. In this paper, we tackle this important problem by developing a simple yet effective model. The major novelty is that we identify two types of outliers and devise efficient detection algorithms. Our experiments on both real data and synthetic data confirm the effectiveness, efficiency and scalability of our approach. The techniques reported in this paper have been in production in a large scale business application.