Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
The Journal of Machine Learning Research
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
On one extremal problem of adaptive machine learning for detection of anomalies
Automation and Remote Control
An optimization model for outlier detection in categorical data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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This paper presents a novel approach to mining patterns and outliers detection in the Web Usage log. This approach involves kernel methods and fuzzy clustering methods. Web log records are considered as vectors with numeric and nominal attributes. These vectors are mapped by means of a special kernel to a high dimensional feature space, where the possibilistic clustering method is used to calculate the measure of "typicalness" of vectors. If the value of this measure for a particular record is less than specified threshold this record is labeled as an outlier. The records with high "typicalness" are considered as access patterns of user activity. The performance of the approach is demonstrated experimentally.