Data mining: concepts and techniques
Data mining: concepts and techniques
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Distance-Based Detection and Prediction of Outliers
IEEE Transactions on Knowledge and Data Engineering
Learning of neural networks for fraud detection based on a partial area under curve
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
| Hi-index | 0.00 |
For many data mining applications, finding the rare instances or the outliers is more interesting than finding the common patterns. At present, many automated outlier detection methods are available, however, most of those are limited by assumptions of a distribution or require upper and lower predefined boundaries in which the data should exist. Whereas a distribution is often unknown, and enough information may not exist about a set of data to be able to determine reliable upper and lower boundaries. For these cases, a new dissimilarity function was defined, which can be viewed as fitness function of genetic algorithm, and a GA-based outlier detection method was formed in this paper. This method allows for detection of multiple outliers, not just one at a time. The illustrations show that the improved approach can automatically detect outliers, and performs better than GLOF approach.