The 1960s included the earliest discussion on an inductive approach towards the construction of a decision tree for the classification of objects that are described by attributes (Hunt, Marin, & Stone, 1966). It is not until over a decade later that the popularization of the decision tree method was materialized with the introduction of what developed into the Interactive Dichotomizer 3 (Quinla...

Due to the increasing occurrence of very large databases, mining useful information and knowledge from transactions is evolving into an important research area. In the past, many algorithms were proposed for mining association rules, most of which were based on items with binary values. Transactions with quantitative values are, however, commonly seen in real-world applications. In this paper, ...

Decision and risk analysts have considerable discretion in designing procedures for eliciting subjective probabilities. One popular approach is to specify a particular set of exclusive and exhaustive events for which the assessor provides subjective probabilities. We show that assessed probabilities are biased toward a uniform distribution over all events into which the relevant sample space ha...

Decision and risk analysts have considerable discretion in designing procedures for eliciting subjective probabilities. One popular approach is to specify a particular set of exclusive and exhaustive events for which the assessor provides subjective probabilities. We show that assessed probabilities are biased toward a uniform distribution over all events into which the relevant sample space ha...

Probabilistic Risk Assessment (PRA) is a method of estimating system reliability by combining logic models of the ways systems can fail with numerical failure rates. One postulates a failure state and systematically decomposes this state into a combination of more basic events through a process known as Fault Tree Analysis (FTA). Failure rates are derived from vendor specifications, historical ...

Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...

Item hierarchy and concept tree provide references for cognition diagnosis and remedial instruction. Therefore, integration of data analysis on item hierarchy and concept tree should be important. The purpose of this study is to provide an integrated methodology of item hierarchy and concept tree analysis. Besides, fuzzy clustering is adopted to classify sample so that homogeneity appear in the...

Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined structures. The behavior of a fuzzy automaton generates language by mapping set regular trees on ranked alphabet to membership values. It calculates the grade using rules that process their structural characteristics. This paper deals with constructing models satisfies given logical propositions (called prop...