Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Research on Data Mining Algorithms for Automotive Customers' Behavior Prediction Problem

Abstract: This paper extracts automotive marketing information, constructs data warehouse, adopts an improved ID3 decision tree model and an association rule model to do data mining, and then obtains prediction information of automotive customers' behavior. Experimental and comparative results verify the validity and accuracy of the prediction results.

Customer Value Analysis Based on Rough Set Data Mining Technique

Abstract: This paper studies data mining algorithm based on classification mode in detail, especially classification rules pick-up based on rough sets and based on construction decision trees. An improving-algorithm of decision tree model based on rough sets is given. The technique of decision tree based on rough sets is used in customer value management fields, measurement customer value and segmentation customers were carried out after the decision tree model based on rough sets was set up by a series of feasible index system. Based on data mining technique, We can provide intelligent decision support for the customer management of enterprise by efficiently identifying customers, evaluating customer value, segmentation customers, improving the sale effect, retaining customers and increasing customers satisfaction and loyalty degree and so on.

Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm

Abstract: Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

Recovery point selection on a reverse binary tree task model

Abstract: An analysis is conducted of the complexity of placing recovery points where the computation is modeled as a reverse binary tree task model. The objective is to minimize the expected computation time of a program in the presence of faults. The method can be extended to an arbitrary reverse tree model. For uniprocessor systems, an optimal placement algorithm is proposed. For multiprocessor systems, a procedure for computing their performance is described. Since no closed form solution is available, an alternative measurement is proposed that has a closed form formula. On the basis of this formula, algorithms are devised for solving the recovery point placement problem. The estimated formula can be extended to include communication delays where the algorithm devised still applies. >

Tree model reconstruction innovization using multi-objective differential evolution

Abstract: This paper presents an innovization design principle for procedural tree model of woody plants (trees) reconstruction by multi-objective optimization. Reconstruction of a parameterized procedural model from imagery is addressed by a multi-objective differential evolution algorithm, which evolves a parametrized procedural model by fitting a set of its rendered images to a set of given projected images using bi-objective comparisons made on a pixel level of the images. The use of multi-objective approach gives the decision maker a chance to select the final resulting model, and helps determine the optimization criteria tradeoff weights for later production.