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With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proven to be(More)
Self-Organizing maps (SOM) have become popular for tasks in data visualization, pattern classification or natural language processing and can be seen as one of the major concepts for artificial neural networks of today. Their general idea is to approximate a high dimensional and previously unknown input distribution by a lower dimensional neural network(More)
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We develop a general algorithm for decomposition and compression of grayscale images. The decomposition can be expressed as a functional relation between the original image and the Hadamard waveforms. The dynamic adaptive clustering procedure incorporates potential functions as a similarity measure for clustering as well as a reclustering phase. The latter(More)
Initialization of self-organizing maps is typically based on random vectors within the given input space. The implicit problem with random initialization is the overlap (entanglement) of connections between neu-rons. In this paper, we present a new method of initiali-zation based on a set of self-similar curves known as Hilbert curves. Hilbert curves can be(More)
Identifying bidders with suspicious bidding activities related to possible online auction fraud is a difficult task due to a large number of users participating in online auctions. In order to reduce the number of users to be investigated, we examine observable features of a bidder's behavior, and utilize a hierarchical clustering technique to divide a(More)