Decision tree construction is an important data mining problem. In this paper, we revisit this problem, with a new goal, i.e. Can we develop an efficient parallel algorithm for decision tree construction that can be parallelized in the same way as algorithms for other major mining tasks ?. We report a new approach to decision tree construction, which we refer to as SPIES (Statistical Pruning of...

This paper introduces a new algorithm for the induction of decision trees, based on adaptive techniques. One of the main feature of this algorithm is the application of automata theory to formalize the problem of decision tree induction and the use of a hybrid approach, which integrates both syntactical and statistical strategies. Some experimental results are also presented indicating that the...

In this research, we have compared three different attribute selection measures algorithms. We have used ID3 algorithm, C4.5 algorithm and CART algorithm. All these algorithms are decision tree based algorithm. We have got the accuracy of three different algorithms and we observed that the accuracy of ID3 algorithm is greater than C4.5 algorithm. But the accuracy of CART algorithm is greater th...

This study aimed to evaluate the effect of type of players on ecosystem accounting system using structural equations. In total, 84 activists in the field of environmental accounting (ecosystem) were selected through convenience sampling. Subjects filled the 22-item questionnaire of components of actor network and the 25-item questionnaire of ecosystem accounting. Given the fact that the signifi...

Using the theory of Web Data Mining, this paper proposes a physics discipline personalized learning evaluation system model based on decision tree algorithm. The overall structure of this system model, which is applied to the network-based education, is presented. The key part of the model, data collection module and personalized evaluation module are introduced. The advantages and disadvantage...

Among the several tasks that evolutionary algorithms have successfully employed, the induction of classification rules and decision trees has been shown to be a relevant approach for several application domains. Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, conventionally used decision trees induction algorithm...

This paper explores two simple and efficient pre-pruning strategies for the cost-sensitive decision tree algorithm to avoid overfitting. One is to limit the cost-sensitive decision trees to a depth of two. The other is to prune the trees with a pre-specified threshold. Empirical study shows that, compared to the error-based tree algorithm C4.5 and several other cost-sensitive tree algorithms, t...

Data mining using integration of clustering and decision tree algorithm has been proposed for predicting the stock market prices. This mechanism involves studying stock price patterns in time by attempting to predict future results of a time-series by simply studying patterns in the time-series of stock prices. The goal of this project is to implement data mining in order to predict the Time-Se...

This thesis describes the optimal minimum spanning tree algorithm given by Pettie and Ramachandran (in Journal of the ACM, 2002). The algorithm presented finds a minimum spanning tree of a graph with n vertices and m edges deterministically in time O (T ∗ (m,n)), where T ∗ is the minimum number of edge-weight comparisons needed to determine the solution of the problem. The function T ∗ is the d...

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the exibility inherent in the fuz...