Computational geometry: an introduction
Computational geometry: an introduction
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Detecting Errors in Foreign Trade Transactions: Dealing with Insufficient Data
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Resource-bounded Outlier Detection using Clustering Methods
Proceedings of the 2010 conference on Data Mining for Business Applications
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This paper describes an approach to fraud detection targeted at applications where this task is followed by a posterior human analysis of the signaled frauds. This is a frequent setup on fraud detection applications (e.g. credit card misuse, telecom fraud, etc.). In real world applications this human inspection is usually constrained by limited resources. In this context, standard fraud detection methods that simply tag each case as being (or not) a possible fraud are not very useful if the number of tagged cases surpasses the available resources. A much more useful approach is to produce a ranking of fraud that can be used to optimize the available inspection resources by first addressing the cases with higher rank. In this paper we propose a method that produces such ranking. The method is based on the output of standard agglomerative hierarchical clustering algorithms, resulting in no significant additional computational costs. Our comparisons with a state of the art method provide convincing evidence of the competitiveness of our proposal.