clustering
A part of the topology validation process in which vertices that fall within a specified distance (cluster tolerance) of each other are snapped together.
This clustering algorithm estimates both the number of distinct subclasses in each class, and the spectral mean and covariance for each subclass. The number of subclasses is estimated using Rissanen's minimum description length (MDL) criteria [1].
Figure 4. Clustering on the latitude and longitude coordinates of point locations can be used to identify geographically balanced customer territories.
This viewpoint is concerned primarily with the interaction between distinct computational objects: its chief concerns are communication, computing systems, software processes and the clustering of computational functions at physical nodes of a ...
Agglomeration Economies (of Scale or Scope) (as different from "regions of agglomeration") Benefits, savings or (average) cost reductions resulting from the clustering of activities.
Simple (in this method the estimation of the mean can be established a priori based upon a different data set from the data used for the present estimation; for example, declustering mean) and ordinary (with unknown mean) krigings (Gandin, 1963) can ...
The map image can be cleaned considerably without removing contour line pixels by combining clustering to reduce the number of colors with the web removal tool.
In unsupervised training, a clustering algorithm is used to partition a sample of the data into populations of pixels with similar reflectance, ...
But if there is no global autocorrelation or no clustering, we can still find clusters at a local level using local spatial autocorrelation.
This is due to the large clustering of hard surfaces that heat up in sunlight and that channel rainwater into underground ducts. As a result, city weather is often windier and cloudier than the weather in the surrounding countryside.
New tools have been introduced to do space-time clustering, sophisticated group analysis, and exploratory regression using spatial parameters. Improvements made in kriging include empirical Bayesian kriging and more effective aerial interpolation.
The concept of clustering is based on the distances between the input vectors. If, as in our case, the elevation samples are regularly distributed the clusters will be also regular.
The classification is based on a clustering algorithm and is very fast. When selecting the number of classes for classification, do not make it too many because regions may get fragmented.
Then there is the system architecture: significant developments in the V8 XM Edition of ProjectWise introduced Network load balancing to ensure high system performance during periods of high demand and server clustering to provide fail-over security ...
Map Design, Fifth Edition, 1999, pages 147-149) relates that the Natural Breaks classes are found by an iterative search to minimize the sum of spreads of the classes. Thus it is a one-dimensional example of the K-means clustering method.
Similarly, in very steep mountainous areas the contours may be more widely spaced to avoid clustering of lines into unreadable masses. The contour interval used on a topographic map is printed below the scale in the map legend.
 See also: Information, Model, Location, Relation, Object
  
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