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.