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We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a pre-processing step without sacrificing efficiency. The key is a partial evaluation...
The real-world process of generating a large spatio-temporal data collection presents a very difficult technical problem. First, this process is very expensive, requiring a lot of various high-technology software tools and modern hardware infrastructure (sensors, servers, GPS infrastructure etc.) installations; second, the recorded trajectories sometimes cannot represent any special traffic or movement...
INGENS is a prototype of GIS which integrates a geographic knowledge discovery engine to mine several kinds of spatial KDD objects from the topographic maps stored in a spatial database. In this paper we describe the main principles of an inductive spatial database in INGENS. Inductive database allows to keep permanent KDD objects and integrate database technology with systems for the geographic knowledge...
Although a huge amount of remote sensing data has been provided by Earth observation satellites, few data manipulation techniques and information extraction in large data sets have been developed. In this context, the present paper aims to show a new system for spatial data mining, and two test cases applied to land use change in the Brazilian Amazon region. We present the operational environment...
Unlike the traditional incremental updating problem for discrete data, the appended data to spatial dataset may introduce lots of new relations between the added events and the existing events. Moreover, as the measure in mining of colocation patterns, participation index is complicated to handle compared with simply support counter. Thus, the incremental maintenance of colocation patterns for dynamic...
We propose a probabilistic model for the relevance feedback of users looking for target images. This model takes into account user errors and user uncertainty about distinguishing similarly relevant images. Based on this model, we have developed an algorithm, which selects images to be presented to the user for further relevance feedback until a satisfactory image is found. In each query session,...
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