Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.

Simultaneous pooled auctions

Suppose a seller wants to sell k similar or identical objects and there are n > k potential buyers. Suppose that each buyer wants only one object. In this case, we suggest the use of a simultaneous auction that would work as follows. Players are asked to submit sealed bids for one object. The individual with the highest bid chooses an object first; the individual with the second-highest bid chooses the next object; and this process continues until the individual with the kth highest bid receives the last object. Each individual pays the equivalent to his or her bid. When objects are identical, we show that the proposed auction generates the same revenue as a first-price sealed-bid sequential auction. When objects are perfectly correlated, there is no known solution for sequential auctions, whereas we can characterize bidding strategies in the proposed auction. Moreover, the proposed auction is optimal (given an appropriately chosen reserve price), and it may be easier and cheaper to run than a sequential auction.