Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...

Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. This paper proposes a method called “RBDT-1”rule based decision tree -for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. RBDT-1 method uses a set of declarative rules a...

as we know that with the help of Data mining techniques we can find out knowledge in terms of various characteristics and patterns. In this regard this paper presents finding out of anomalies/ outliers using various decision tree based classifiers viz. Best-first Decision Tree, Functional Tree, Logistic Model Tree, J48 and Random Forest decision tree. Three real world datasets has been used in ...

Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques. Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people re...

Panic attacks are discrete episodes of intense fear or discomfort accompanied by symptoms such as palpitations, shortness of breath, sweating, trembling, derealization and a fear of losing control or dying. Although panic attacks are required for a diagnosis of panic disorder, they also occur in association with a host of other disorders listed in the 5h version of the diagnostic and statistica...

We present Tree, a new approach to structural classification. This integrated approach induces decision trees that test for pattern occurrence in the inner nodes. It combines state-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, Tree uses no heuristics and only a single, statistically well founde...

risk response planning is one of the main phases in the project risk management and has major impacts on the success of a large-scale project. since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. the conventional approaches tend to be less effective in dealing with the impreciseness of risk response pl...

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “no...