643 research outputs found
ClustGeo: an R package for hierarchical clustering with spatial constraints
In this paper, we propose a Ward-like hierarchical clustering algorithm
including spatial/geographical constraints. Two dissimilarity matrices
and are inputted, along with a mixing parameter . The
dissimilarities can be non-Euclidean and the weights of the observations can be
non-uniform. The first matrix gives the dissimilarities in the "feature space"
and the second matrix gives the dissimilarities in the "constraint space". The
criterion minimized at each stage is a convex combination of the homogeneity
criterion calculated with and the homogeneity criterion calculated with
. The idea is then to determine a value of which increases the
spatial contiguity without deteriorating too much the quality of the solution
based on the variables of interest i.e. those of the feature space. This
procedure is illustrated on a real dataset using the R package ClustGeo
Fuzzy clustering with spatial-temporal information
Clustering geographical units based on a set of quantitative features observed at several time occasions requires to deal with the complexity of both space and time information. In particular, one should consider (1) the spatial nature of the units to be clustered, (2) the characteristics of the space of multivariate time trajectories, and (3) the uncertainty related to the assignment of a geographical unit to a given cluster on the basis of the above com- plex features. This paper discusses a novel spatially constrained multivariate time series clustering for units characterised by different levels of spatial proximity. In particular, the Fuzzy Partitioning Around Medoids algorithm with Dynamic Time Warping dissimilarity measure and spatial penalization terms is applied to classify multivariate Spatial-Temporal series. The clustering method has been theoretically presented and discussed using both simulated and real data, highlighting its main features. In particular, the capability of embedding different levels of proximity among units, and the ability of considering time series with different length
Image inpainting based on self-organizing maps by using multi-agent implementation
AbstractThe image inpainting is a well-known task of visual editing. However, the efficiency strongly depends on sizes and textural neighborhood of âmissingâ area. Various methods of image inpainting exist, among which the Kohonen Self-Organizing Map (SOM) network as a mean of unsupervised learning is widely used. The weaknesses of the Kohonen SOM network such as the necessity for tuning of algorithm parameters and the low computational speed caused the application of multi- agent system with a multi-mapping possibility and a parallel processing by the identical agents. During experiments, it was shown that the preliminary image segmentation and the creation of the SOMs for each type of homogeneous textures provide better results in comparison with the classical SOM application. Also the optimal number of inpainting agents was determined. The quality of inpainting was estimated by several metrics, and good results were obtained in complex images
Fuzzy clustering of spatial interval-valued data
In this paper, two fuzzy clustering methods for spatial intervalvalued
data are proposed, i.e. the fuzzy C-Medoids clustering
of spatial interval-valued data with and without entropy regularization.
Both methods are based on the Partitioning Around
Medoids (PAM) algorithm, inheriting the great advantage of
obtaining non-fictitious representative units for each cluster.
In both methods, the units are endowed with a relation
of contiguity, represented by a symmetric binary matrix. This
can be intended both as contiguity in a physical space and as
a more abstract notion of contiguity. The performances of the
methods are proved by simulation, testing the methods with
different contiguity matrices associated to natural clusters of
units. In order to show the effectiveness of the methods in
empirical studies, three applications are presented: the clustering
of municipalities based on interval-valued pollutants levels, the
clustering of European fact-checkers based on interval-valued
data on the average number of impressions received by their
tweets and the clustering of the residential zones of the city of
Rome based on the interval of price values
Fuzzy clustering of spatial interval-valued data
In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy
C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster.
In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values
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