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 necessary to process this type of increasing workload. In this paper, we first present a framework of Real-Time Self-Adaptive Classifier (RT-SAC) for identifying suspicious bidders in online auctions using an incremental neural network approach. Then we introduce a clustering module that characterizes bidder behaviors in measurable attributes and uses a hierarchical clustering mechanism to create training datasets. The neural network in RT-SAC is initialized with the training datasets, which consist of labeled historical auction data.Once initialized, the network can be trained incrementally to gradually adapt to new bidding data in real-time, and thus, it supports efficient detection of suspicious bidders in online auctions. Finally, we utilize a case study to demonstrate how parameters in RT-SAC can be tuned for optimal operations and how our approach can be used to effectively identify suspicious online bidders in real-time.
In this paper, 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 is a multi-iteration, convergent procedure which divides the inputs into nonoverlapping clusters. These two techniques allow us to efficiently store and transmit a class of half-tone medical images such as magnetic resonance imaging (MRI) of the human brain. Due to the redundant image structure of MRI, obtained after the decomposition and clustering, almost half of the image can be omitted all together. Naturally, the compression rates for this specific type of grayscale image are increased greatly. A run-length coding is performed in order to compress further the retained information from the first two steps. Although all the techniques applied are simple, they represent an efficient way to compress grayscale images. The algorithm exhibits a performance which is competitive and often outperforming some of the methods reported in the literature.
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