Network analysis of online bidding activity

With the advent of digital media, people are increasingly resorting to online channels for commercial transactions. Online auction is a prototypical example. In such online transactions, the pattern of bidding activity is more complex than traditional online transactions; this is because the number of bidders participating in a given transaction is not bounded and the bidders can also easily respond to the bidding instantaneously. By using the recently developed network theory, we study the interaction patterns between bidders (items) who (that) are connected when they bid for the same item (if the item is bid by the same bidder). The resulting network is analyzed by using the hierarchical clustering algorithm, which is used for clustering analysis for expression data from DNA microarrays. A dendrogram is constructed for the item subcategories; this dendrogram is compared with a traditional classification scheme. The implication of the difference between the two is discussed.Comment: 8 pages and 11 figure

Rough Sets Clustering and Markov model for Web Access Prediction

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation