Searching for a binary partition of attribute domains is an important task in data mining. It is present in both decision tree construction and discretization. The most important advantages of decision tree methods are compactness and clearness of knowledge representation as well as high accuracy of classification. Decision tree algorithms also have some drawbacks. In cases of large data tables...

Simple techniques for the development and use of decision tree classi ers assume that all attribute values of all cases are available Numerous approaches have been proposed with the aim of extending these techniques to cover real world situations in which unknown attribute values are not uncommon This paper compares the e ectiveness of several approaches as measured by their performance on a co...

AP, STEMI and NSTEMI are the main categories of acute coronary syndrome which causes damage to the coronaries and make the patients prone to high risk of death. Several studies with different technologies have been made in diagnosis and treatment of the events, which includes association rules, logistic regression, fuzzy modeling, and neural network, CART, ID3. The existing techniques are confi...

A DT is a classification scheme which generates a tree and a set of rules, representing the model of different classes, from a given dataset. As per Hans and Kamber [HK01], DT is a flow chart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test and leaf nodes represent the classes or class distributions. The top most node in...

Attack–defense trees are a novel methodology for graphical security modeling and assessment. The methodology includes visual, intuitive tree models whose analysis is supported by a rigorous mathematical formalism. Both, the intuitive and the formal components of the approach can be used for quantitative analysis of attack–defense scenarios. In practice, we use intuitive questions to ask about a...

To cope with the problem of input distortion by users of Web-based expert systems, we develop methods to distinguish liars from truth-tellers based on verifiable attributes, and redesign the expert systems to control the impact of input distortion. The four methods we propose are termed split tree, consolidated tree, value based split tree, and value based consolidated tree. They improve the pe...

DNF systems divide the input space into decision surfaces, defined by conjunctions of conditions based on attribute-value pairs. This means that the decision surfaces are orthogonal to the axis of the tested attribute and parallel to all the other axes. In this paper, we present the system Ltree, that is able to define decision surfaces both orthogonal and oblique to the axes defined by the att...

Decision trees have been found to be very effective for classification in the emerging field of data mining. This paper proposes a new method: CC-SLIQ (Cascading Clustering and Supervised Learning In Quest) to improve the performance of the SLIQ decision tree algorithm. The drawback of the SLIQ algorithm is that in order to decide which attribute is to be split at each node, a large number of G...

This paper presents a data mining project that generated Bayesian models to assess risk of corruption of federal management units. With thousands of extracted features related to corruptibility, the data were processed using techniques like correlation analysis and variance per class. We also compared two different discretization methods: Minimum Description Length Principle (MDLP) and Class-At...

An attribute-oriented induction has been developed in the previous study of knowledge discovery in databases. A concept tree as-cension technique is applied in concept generalization. In this paper, we extend the background knowledge representation from an unconditional non-rule-based concept hierarchy to a rule-based concept hierarchy , which enhances greatly its representation power. An eecie...