Attribute clustering has been previously employed to detect statistical dependence between subsets of variables. We propose a novel attribute clustering algorithm motivated by research of complex networks, called the Star Discovery algorithm. The algorithm partitions and indirectly discards inconsistent edges from a maximum spanning tree by starting appropriate initial modes, therefore generati...

We describe a new access method, the hB”-tree, an adaptation of the hB-tree index to the constraints of the II-tree . The II-trees, a generalization of the Blink-trees, provide high concurrency with recovery, because they break down structure modification into a series of short atomic actions. In addition, the IItrees include a node consolidation algorithm. The hB-tree is, a multi-attribute ind...

Attributed tree transducers are abstract models used to study properties of attribute grammars. One abstraction which occurs when modeling attribute grammars by attributed tree transducers is that arbitrary trees over a ranked alphabet are taken as input, instead of derivation trees of a context-free grammar. In this paper we show that with respect to the generating power this is not an abstrac...

An attribute grammar is pure (left-to-right) multi-pass if a bounded number of left-to-right passes over the derivation tree suffice to compute all its attributes. There is no requirement, as for the usual multi-pass attribute grammars, that all occurrences of the same attribute are computed in the same pass, R is shown that the problem of determining whether an arbitrary attribute grammar is p...

Let AG be an attribute grammar, with underlying context free grammar G and attribute evaluation rules A . The function that decorates —according to A— a parse tree with attribute values and then delivers the synthesized attribute value of the root node, is denoted [[A]] . We translate G into a functor F such that any parse tree for G is an element of the initial F -algebra. The attribute evalua...

This paper studies the eeects on decision tree learning of constructing four types of attribute (conjunc-tive, disjunctive, M-of-N, and X-of-N representations). To reduce eeects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With diierent option settings, it can construc...

The attribute dependence graph of a syntax tree may be partitioned into disjoint regions. Attribute instances in di€erent regions are independent of one other. The advantages of partitioning the attribute dependence graph include simplifying the attribute grammar conceptually and allowing the possibility of parallel evaluation. We present a static partitioning algorithm for attribute grammars. ...

Classification is a data mining (DM) technique used to predict or forecast the unknown information using the historical data. There are many classification techniques. ID3 is a very popular tree based classification algorithm for a categorical data which does not support continuous data. Attribute selection process plays major role in building a classification tree model. Attribute Selection in...

In the PESTO file system, the complete file update history is kept as a tree of file versions. It will often be desirable to give access to subsets of the file versions history without having to grant access to the entire tree. Keys on Trees is a scheme for assigning attributes to every tree node in such a way that knowledge of the attribute of one node makes it possible to calculate the attrib...

Attribute grammars are a formalism for specifying computations on syntax trees. SSCC (a Sufficiently Smart Compiler Compiler) is a practical attribute-grammar system based on a polynomial-time extension to Kastens’s ordered attribute grammars. The new class of attribute grammars is strictly larger than the class of ordered attribute grammars, and it retains the property that there is a polynomi...