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Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electromagnetic media. We propose an exploratory method that reveals a robust clustering hierarchy. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require any prior knowledge about the data set, nor does it require parameters(More)
We incorporate two knowledge discovery techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns. This tool is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data. We present two methods for this exploratory analysis and we(More)
To support the need for interactive spatial analysis, it is often necessary to rethink the data structures and algorithms underpinning applications. This paper describes the development of an interactive environment in which a number of different Voronoi models of space can be manipulated together in real time, to (1) study their behaviour, (2) select(More)
With the development of web technique and social network sites human now can produce information, share with others online easily. Photo-sharing website, Flickr, stores huge number of photos where people upload and share their pictures. This research proposes a framework that is used to extract associative points-of-interest patterns from geo-tagged photos(More)
This paper describes a series of dynamic update methods that can be applied to a family of Voronoi diagram types, so that changes can be updated incrementally, without the usual recourse to complete reconstruction of their underlying data structure. More efficient incremental update methods are described for the ordinary Voronoi diagram, the farthest-point(More)
In many GIS settings, the Euclidean metric is not applicable as the model for distance between points. Other geometric models are needed in many practical scenarios, for which urban geography is a common example. Recently, Estivill-Castro and Lee [8] proposed an effective and efficient boundary-based clustering method overcoming drawbacks of traditional(More)