Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of convention...

Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of convention...

We prove the computational hardness of three k-clustering problems using an (almost) arbitrary Bregman divergence as dissimilarity measure: (a) The Bregman k-center problem, where the objective is to find a set of centers that minimizes the maximum dissimilarity of any input point towards its closest center, and (b) the Bregman k-diameter problem, where the objective is to minimize the maximum ...

We present an interactive visualization technique for spatial probability density function data. These are datasets that represent a spatial collection of random variables, and contain a number of possible outcomes for each random variable. It is impractical to visualize all the information at each spatial location as it will quickly lead to a cluttered image. We advocate the use of hierarchica...

The bounded k-median problem is to select in an undirected graph G = (V, E) a set S of k vertices such that the distance from any vertex v ∈ V to S is at most a given bound d and the average distance from vertices V\S to S is minimized. We present randomized algorithms for several versions of this problem and we prove some inapproximability results. We also study the bounded version of the unca...

In the classical k-median problem the goal is to select a subset of at most k facilities in order to minimize the total cost of opened facilities and established connections between clients and opened facilities. We consider the capacitated version of the problem, where a single facility may only serve a limited number of clients. We construct approximation algorithms slightly violating the cap...

Collaborative spatial decision-making environments in which group members individually and collectively pursue solutions to ill-structured problems have a unique set of cartographic visualization requirements. In this paper we restrict our focus to the domain of facility location problems and describe several new map types that are designed to support the process of making comparisons among alt...

This paper describes our contributions to the Social Event Detection (SED) task as part of the MediaEval Benchmark 2014. We first present an unsupervised approach for the clustering of social events that builds solely on provided metadata. Results show that already the use of available time and location information achieves high clustering precision. In the next step, we focus on the retrieval ...

residential location choice modeling is one of the areas in transportation planning that attempts to examine households location search behavior incorporating their trade-offs between housing quality, prices or rents, distance to work and other key factors. this brings up the need to come up with methods to logically allocate credible choice alternatives for individuals.this article attempts to...