This paper presents the use of hierarchical spatial data structures to visualise uncertainty in spatial information. This is demonstrated using spatial data from the New Zealand 2001 Census. Current visualisation of uncertainty techniques are evaluated, and a new method is proposed. This paper assesses the value of hierarchical tree structures as a geovisualisation of attribute uncertainty tech...

Typically, decision tree construction algorithms apply a single \goodness of split" criterion to form each test node of the tree. It is a hypothesis of this research that better results can be obtained if during tree construction one applies a split criterion suited to the \location" of the test node in the tree. Speciically, given the objective of maximizing predictive accuracy, test nodes nea...

We present a technique to prevent the construction of intermediate data structures in functional programs, which is based on results from the theory of tree transducers. We rst decompose function deen-itions, which correspond to macro tree transducers, into smaller pieces. Under certain restrictions these pieces can be composed to an attributed tree transducer using a composition result for att...

In this chapter methods of handling missing attribute values in data mining are described. These methods are categorized into sequential and parallel. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowledge is acquired for a data set with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge...

By access structure and attribute set, attribute-based encryption realizes fine-grained access control and one to many encryption. The expression of access structure directly decides the application range of one scheme and the general circuit reaches the best form. Since the safety of multilinear maps is suffered question, using relatively efficient and safe bilinear maps to construct circuit a...

The paper analyzes the modular structure of product family by decomposing the product family into generic modules. Then, the module model denoted by attribute variable is established for each generic module. Based on the classification of customer requirement and erection of product decision tree, the description and explanation type of customer requirement can be used to restrict the sub branc...

We consider the problem of learning to classify partially specified instances i.e., instances that are described in terms of attribute values at different levels of precision, using user-supplied attribute value taxonomies (AVT). We formalize the problem of learning from AVT and data and present an AVT-guided decision tree learning algorithm (AVT-DTL) to learn classification rules at multiple l...

There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a con...

Dendrograms and minimum-weight spanning trees (MWST) are discrete structures which arise in clustering theory, networks, and where strategic choices are made between discrete options. The discrete structure of the solution tree varies discontinuously with respect to changes in the dissimilarity or cost matrix. Tarjan [1982] has obtained bounds on the changes to the elements of the cost matrix, ...

syntax, 28abstract syntax tree, 14aspect, 15assumptions, 37AST, 14attribute, 15attributes, 15