Clustering is a widespread method to explore patterns in large spatio-temporal datasets. Most clustering studies are, however, performed either from a spatial or from a temporal point of view. This is sub-optimal because patterns explored from a spatial perspective cannot describe the time-varying behavior present in the dataset and vice versa. Here we illustrate a co-clustering-based analysis ...

In the last decade, the number of logistic service providers located in clusters across Europe has increased substantially. Because location choice models in logistics studies are not able to give a satisfactory explanation for this phenomenon, various spatial economic concepts are studied in this paper. The main purpose is to provide more insight into the selection of economically attractive l...

Contributions from researchers in Knowledge Discovery are producing essential tools in order to better understand the typically large amounts of spatial data in Geographical Information Systems. Clustering techniques are proving to be valuable in providing exploratory analysis functionality while supporting approaches for automated pattern discovery in spatially referenced data and for the iden...

In recalling a list of previously experienced items, participants are known to organize their responses on the basis of the items' semantic and temporal similarities. Here, we examine how spatial information influences the organization of responses in free recall. In Experiment 1, participants studied and subsequently recalled lists of landmarks. In Experiment 2, participants played a game in w...

Abstra t We investigate the use of biased sampling a ording to the density of the dataset, to speed up the operation of general data mining tasks, su h as lustering and outlier dete tion in large multidimensional datasets. In density-biased sampling, the probability that a given point will be in luded in the sample depends on the lo al density of the dataset. We propose a general te hnique for ...

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the stren...

Clustering has been utilized a great deal in statistical data mining as well as machine learning in case of unsupervised learning. Clustering is a centeral task in knowledge discovery and data mining. One of the limitations that clustering methods usually encounter is prede ned number of clusters. The method that is going to be presented in this paper is not constrained by such a limitation. Ge...

Clustering spatial data is a well-known problem that has been extensively studied to find hidden patterns or meaningful sub-groups, it is the problem of grouping data based on similarity and has many applications such as satellite imagery, geographic information systems, medical image analysis, pattern recognition, data clustering and signal processing. While this problem has attracted the atte...