Data Mining - Concepts and Techniques.

Acts in what Hutchinson (1965) has called the 'ecological theatre' are played out on various scales of space and time. To understand the drama, we must view it on the appropriate scale. Plant ecologists long ago recognized the importance of sampling scale in their descriptions of the dispersion or distribution of species (e.g. Greig-Smith, 1952). However, many ecologists have behaved as if patterns and the processes that produce them are insensitive to differences in scale and have designed their studies with little explicit attention to scale. Kareiva & Andersen (1988) surveyed nearly 100 field experiments in community ecology and found that half were conducted on plots no larger than 1 m in diameter, despite considerable differences in the sizes and types of organisms studied. Investigators addressing the same questions have often conducted their studies on quite different scales. Not surprisingly, their findings have not always matched, and arguments have ensued. The disagreements among conservation biologists over the optimal design of nature reserves (see Simberloff, 1988) are at least partly due to a failure to appreciate scaling differences among organisms. Controversies about the role of competition in structuring animal communities (Schoener, 1982; Wiens, 1983, 1989) or about the degree of coevolution in communities (Connell, 1980; Roughgarden, 1983) may reflect the

https://www.jstor.org/stable/info/2389612

Spatial Scaling in Ecology

Thirty-seven programs that summarize data with histograms and other graphics, calculate measures of spatial continuity, provide smooth least-squares-type maps, and perform stochastic spatial simulation.

GSLIB: Geostatistical Software Library and User's Guide

From the Publisher:
Artificial "neural networks" are now widely used as flexible models for regression classification applications, but questions remain regarding what these models mean, and how they can safely be used when training data is limited. Bayesian Learning for Neural Networks shows that Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional neural network learning methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. Use of these models in practice is made possible using Markov chain Monte Carlo techniques. Both the theoretical and computational aspects of this work are of wider statistical interest, as they contribute to a better understanding of how Bayesian methods can be applied to complex problems. Presupposing only the basic knowledge of probability and statistics, this book should be of interest to many researchers in statistics, engineering, and artificial intelligence. Software for Unix systems that implements the methods described is freely available over the Internet.

Bayesian learning for neural networks

On robust estimation of the location parameter

Causality Principle, Deterministic Laws and Chaos.- The Backbone of Fractals: Feedback and the Iterator.- Classical Fractals and Self-Similarity.- Lim and Self-Similarity.- Length, Area and Dimension: Measuring Complexity and Scaling Properties.- Encoding Images by Simple Transformations.- The Chaos Game: How Randomness Creates Deterministic Shapes.- Recursive Structures: Growing Fractals and Plants.- Pascal's Triangle: Cellular Automata and Attractors.- Irregular Shapes: Randomness in Fractal Constructions.- Deterministic Chaos: Sensitivity, Mixing, and Periodic Points.- Order and Chaos: Period-Doubling and Its Chaotic Mirror.- Strange Attractors: The Locus of Chaos.- Julia Sets: Fractal Basin Boundaries.- The Mandelbrot Set: Ordering the Julia Sets.

Chaos and Fractals: New Frontiers of Science

Contents: Foreword: People and Events Behind the "Science of Fractal Images".- Fractals in Nature: From Characterization to Simulation.- Algorithms for Random Fractals.- Color Plates and Captions.- Fractal Patterns Arising in Chaotic Dynamical Systems.- Fantastic Deterministic Fractals.- Fractal Modelling of Real World Images.- Fractal Landscapes Without Creases and with Rivers.- An Eye for Fractals.- A Unified Approach to Fractal Curves and Plants.- Exploring the Mandelbrot Set.- Bibliography.- Index.

The science of fractal images

For about 200 years now mathematicians have developed the theory of smooth curves and surfaces in two, three or higher dimensions. These are curves and surfaces that globally may have a very complicated structure but in small neighborhoods they are just straight lines or planes. The discipline that deals with these objects is differential geometry. It is one of the most evolved and fascinating subjects in mathematics. On the other hand fractals feature just the opposite of smoothness. While the smooth objects do not yield any more detail on smaller scales a fractal possesses infinite detail at all scales no matter how small they are. The fascination that surrounds fractals has two roots: Fractals are very suitable to simulate many natural phenomena. Stunning pictures have already been produced, and it will not take very long until an uninitiated observer will no longer be able to tell whether a given scene is natural or just computer simulated. The other reason is that fractals are simple to generate on computers. In order to generate a fractal one does not have to be an expert of an involved theory such as calculus, which is necessary for differential geometry. More importantly, the complexity of a fractal, when measured in terms of the length of the shortest computer program that can generate it, is very small.

Algorithms for random fractals

The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.

/pdf/feature-based-similarity-search-in-3d-object-databases-2smlg2mk38.pdf

Feature-based similarity search in 3D object databases

We present tools for 3D object retrieval in which a model, a polygonal mesh, serves as a query and similar objects are retrieved from a collection of 3D objects. Algorithms proceed first by a normalization step (pose estimation) in which models are transformed into a canonical coordinate frame. Second, feature vectors are extracted and compared with those derived from normalized models in the search space. Using a metric in the feature vector space nearest neighbors are computed and ranked. Objects thus retrieved are displayed for inspection, selection, and processing. For the pose estimation we introduce a modified Karhunen-Loeve transform that takes into account not only vertices or polygon centroids from the 3D models but all points in the polygons of the objects. Some feature vectors can be regarded as samples of functions on the 2-sphere. We use Fourier expansions of these functions as uniform representations allowing embedded multi-resolution feature vectors. Our implementation demonstrates and visualizes these tools.

/pdf/tools-for-3d-object-retrieval-karhunen-loeve-transform-and-56031xz4kh.pdf

Dietmar Saupe

Papers

Chaos and Fractals: New Frontiers of Science

The science of fractal images

Algorithms for random fractals

Feature-based similarity search in 3D object databases

Tools for 3D-object retrieval: Karhunen-Loeve transform and spherical harmonics