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For the vast majority of the DEM interpolation algorithms, the interpolation process is carried out in the local area, which is largely dependent on terrain features of the dataset of sampling points within the local area. This paper selects surface roughness and spatial distribution indicators to establish the descriptive model of local terrain features, which is then used to describe the characteristics...
Wireless Sensor Networks (WSN), consisting of a large number of sensor nodes connected through wireless medium has emerged as a ground-breaking technology that offers the unprecedented ability to monitor the physical world accurately. Because of resource-constrained nature of sensor nodes, a number of issues have emerged out of which energy-efficiency is an important matter of concern. In this work,...
In the basic idea of k-means clustering algorithm and its criterion function, step process, the cluster analysis theory and method into intelligent design of high-rise structure was introduced in this paper, the high-rise structure of intelligent design case retrieval method based on the K-Means clustering analysis method was established, and by the given engineering application, the process of clustering...
In this paper, it firstly establishes the assessment contents on uncertainty of multi-scale representation of point cluster around the process of multi-scale representation of point cluster and its spatial analysis, which includes positional uncertainty and uncertainty of spatial analysis conclusions; the assessment index K and the quantitative model of positional uncertainty related to point cluster...
Quality control using scalar quality measures is standard practice in manufacturing. However, there are also quality measures that are determined at a large number of positions on a product, since the spatial distribution is important. We denote such a mapping of local coordinates on the product to values of a measure as a measurement map. In this paper, we examine how measurement maps can be clustered...
In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an...
Knowledge of wetland use of migratory bird species during the annual life circle is important to construct conservation strategy and explore the implication for avian influenza control. Biological scientists have used GPS satellite telemetry to determine the habitat of wild birds. However, because there is not an efficient method to process the location data sets, scientists have to devote themselves...
In content-based image retrieval, how to representation of local properties in an image is one of the most active research issues. In certain circumstance, however, users concern more about objects of their interest and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background). Previous work on represent of local properties normally requires...
In this paper, we present a new approach for automatic color image segmentation. It is a multilevel clustering method based on a new proposed non-parametric clustering algorithm, called adaptive medoidshift (AMS) and normalized cuts (N-cut). The AMS algorithm is a modification of recently presented medoidshift algorithm by transforming its global fixed bandwidth to local automatically chosen bandwidth...
In this paper, we present new perceptual techniques for segmentation and annotation of natural images. The image segmentation approach is a multilevel clustering method based on a new proposed non-parametric clustering algorithm, called adaptive medoidshift (AMS) and normalized cuts (N-cut). The AMS method locally clusters the image color composition by considering their spatial distribution into...
We propose an adaptive constrained clustering (ACC) algorithm that performs clustering and feature weighting simultaneously and that can incorporate partial supervision information. This information consists of a set of constraints on which instances should or should not reside in the same cluster. The algorithm is dynamic in the sense that the optimal number of clusters can expand or shrink depending...
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