Application Comparison of Association Rules and C4.5 Rules in Land Evaluation

Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong Province.The results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on C4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation.

The Convergence of the Steepest Descent Algorithm for D.C.Optimization

Some properties of a class of quasi-differentiable functions（the difference of two finite convex functions） are considered in this paper. And the convergence of the steepest descent algorithm for unconstrained and constrained quasi-differentiable programming is proved.

A Hybrid Multifarious Clustering Algorithm for the Analysis of Memmogram Images

A number of clustering algorithms were used to analyze many databases in the field of image clustering. The main objective of this research work was to perform a comparative analysis of the two of the existing partitions based clustering algorithms and a hybrid clustering algorithm. The results verification done by using classification algorithms via its accuracy. The perfor-mance of clustering and classification algorithms were carried out in this work based on the tumor identification, cluster quality and other parameters like run time and volume complexity. Some of the well known classification algorithms were used to find the accuracy of produced results of the clustering algorithms. The performance of the clustering algorithms proved mean-ingful in many domains, particularly k-Means, FCM. In addition, the proposed multifarious clustering technique has revealed their efficiency in terms of performance in predicting tumor affected regions in mammogram images. The color images are converted in to gray scale images and then it is processed. Finally, it is identified the best method for the analysis of finding tumor in breast images. This research would be immensely useful to physicians and radiologist to identify cancer affected area in the breast.

Study on Prediction Model of Number of Rainstorm Days in Summer Based on C5.0 Decision Tree Algorithm

Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.

An Active Rule Approach for Network Intrusion Detection with Enhanced C4.5 Algorithm

Intrusion detection systems provide additional defense capacity to a networked information system in addition to the security measures provided by the firewalls. This paper proposes an active rule based enhancement to the C4.5 algorithm for network intrusion detection in order to detect misuse behaviors of internal attackers through effective classification and decision making in computer networks. This enhanced C4.5 algorithm derives a set of classification rules from network audit data and then the generated rules are used to detect network intrusions in a real-time environment. Unlike most existing decision tree based approaches, the spawned rules generated and fired in this work are more effective because the information-theoretic approach minimizes the expected number of tests needed to classify an object and guarantees that a simple (but not necessarily the simplest) tree is found. The main advantage of this proposed algorithm is that the generalization ability of enhanced C4.5 decision trees is better than that of C4.5 decision trees. We have employed data from the third international knowledge discovery and data mining tools competition (KDDcup’99) to train and test the feasibility of this proposed model. By applying the enhanced C4.5 algorithm an average detection rate of 93.28 percent and a false positive rate of 0.7 percent have respectively been obtained in this work.

基于C4.5决策树的电网故障设备状态评估

对电网故障设备进行准确的状态评估可有效提高产品质量,减少资源浪费。提出了一种基于C4.5决策树的电网故障设备状态评估方法。首先,通过对电网设备故障因素进行分析,构建了包含13个状态量的设备故障状态体系。其次,基于C4.5决策树算法构建了电网设备状态评估模型。最后,通过后剪枝算法对模型进行剪枝降低模型复杂度,并定义多项指标对算法模型进行评价。实际数据分析结果表明,构建的算法模型预测准确率、精确率均能够达到91%以上,有效支撑设备厂商储备备品备件方面工作,提升企业效益。

A New Speculative Execution Algorithm Based on C4.5 Decision Tree for Hadoop

As a distributed computing platform, Hadoop provides an effective way to handle big data. In Hadoop, the completion time of job will be delayed by a straggler. Although the definitive cause of the straggler is hard to detect, speculative execution is usually used for dealing with this problem, by simply backing up those stragglers on alternative nodes. In this paper, we design a new Speculative Execution algorithm based on C4.5 Decision Tree, SECDT, for Hadoop. In SECDT, we speculate completion time of stragglers and also of backup tasks, based on a kind of decision tree method: C4.5 decision tree. After we speculate the completion time, we compare the completion time of stragglers and of the backup tasks, calculating their differential value, and selecting the straggler with the maximum differential value to start the backup task.Experiment result shows that the SECDT can predict execution time more accurately than other speculative execution methods, hence reduce the job completion time.

Decision Tree and Naive Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing

Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.

ALGORITHMS FOR A SYSTEM OF GENERALIZED MIXED EQUILIBRIUM PROBLEMS AND A COUNTABLE FAMILY OF SOME NONLINEAR MULTI-VALUED NONEXPANSIVE-TYPE MAPS

In this paper, relaxed iterative algorithms of Krasnoselskii-type and Halpern-type that approximate a solution of a system of a generalized mixed equilibrium problem anda common fixed point of a countable family of totally quasi-C-asymptotically nonexpansivemulti-valued maps are constructed. Strong convergence of the sequence generated by thesealgorithms is proved in uniformly smooth and strictly convex real Banach spaces with Kadec-Klee property. Furthermore, several applications of our theorems are also presented. Finally,our theorems are significant improvements on several important recent results for this classof nonlinear problems.

Climate Services Elaboration for Cocoa Cultivation in Côte d’Ivoire: Contribution of CORDEX Climate Projections

This study assessed the contribution of climate projections to improving rainfall information for cocoa crops in the central and southern regions of Côte d’Ivoire. Particular attention was paid to fourteen localities in these two climatic zones. Simulation data were obtained from the CORDEX ensemble and observation data from CHIRPS. They cover the period 1991-2005 for the reference period and the future period from 2021 to 2050 for the RCP4.5 and RCP8.5 scenarios. In addition, the study was based on the water requirements necessary during the critical phase of the cocoa tree (the flowering phase) for a good yield from the cocoa production chain on the one hand, and on a selection of three climate indices CDD, CWD and r95PTOT to study their spatio-temporal changes over two future periods 2021-2035 (near future) and 2036-2050 (medium-term) on the other. These climatic indices influence cocoa cultivation and their use in studies of climatic impacts on agriculture is of prime importance. The analysis of their spatio-temporal changes in this work also contributes to providing climate services based on rainfall, to which cocoa crops are highly sensitive. Our results show that the CDD and CWD indices vary from one region to another depending on latitude. For the fourteen localities studied, the number of consecutive dry days (CDD) could increase between now and 2050, while the number of consecutive wet days (CWD) could decrease over the period 2021-2035 and then increase over the period 2036-2050. The localities of Tabou, Aboisso and San-Pedro record high numbers of CDD index and CWD index for both projection scenarios. In comparison with the RCP4.5 and RCP8.5 scenarios, these results show that the RCP8.5 scenarios are having an impact on cocoa growing in Côte d’Ivoire.

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

Optimal Approximation Algorithms for Reoptimization of Constraint Satisfaction Problems

The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation (approximation ratio) of an algorithm for determining optimal or close to it solutions of some “minor” changes of instance I. To solve the problem Ins-Max-EkCSP-P (reoptimization of Max-EkCSP-P with the addition of one constraint) with approximation resistant predicate P exists a polynomial threshold (optimal) -approximation algorithm, where the threshold “random” approximation ratio of P). When the unique games conjecture (UGC) is hold there exists a polynomial threshold (optimal) -approximation algorithm (where and the integrality gap of semidefinite relaxation of Max-EkCSP-P problem Z) to solve the problem Ins-Max-EkCSP-P.

基于C4.5决策树算法构建学生画像模型的研究与实现

近年来,随着大数据分析技术的发展,用户画像技术日趋成熟,挖掘数据中的隐藏信息已经成了研究热门。文章参考用户画像技术,提出了一种基于C4.5决策树算法的学生画像建模方法。文章以广西大学计算机与电子信息学院的本科生为研究样本,搜集学生们的行为数据制作数据集。该模型采用平均值填充法进行数据预处理,并将清洗完毕的数据投入训练模型进行调参,从而获得最后的测试模型。该模型能够按照三级标签体系将学生分类,准确率达到62.3%。最后该文对研究结果进行了分析总结,确定了未来的发展方向。

应用C4.5算法构造客户分类决策树的方法

客户等级划分是CRM中一个非常重要的方面，而决策树是进行分类分析的一个常用工具，该文主要讨论如何应用C4.5算法构造客户等级分类决策树及其在CRM中的应用。

C4.5算法在2型糖尿病分类规则建立中的应用

基于大量实测数据 ,将数据挖掘C4.5算法引入 2型糖尿病的数据处理之中 ,建立了多条有效规则 ,通过测试其患病与未患病的平均正确识别率达 97%。重要的是建立的这些规则同医学上的认识基本一致 ,可以说这些规则的建立为解释糖尿病发病因素之间的相互关系以及影响程度提供了一种新的方法 ,为建立

C4.5算法在在线学习行为评估系统中的应用

基于数据挖掘技术构建了在线学习行为评估系统。利用C4.5算法挖掘学生在线学习行为与学习效果的历史数据,建立“学习行为—效果”模型。用此模型评估未来学生的在线学习状况,给出形成性评估的成绩。通过测试,评估结果较准确,教师和学生的认可度达90%以上。系统的应用为在远程教学环境下客观、公正、合理的评估学生在线学习提供了有效方法和手段。

基于C4.5算法的道路网网格模式识别

提出一种基于C4.5算法的网格模式识别方法。该方法以道路网中的网眼为基本单元,根据上下文关系将其标识为属于网格模式和不属于网格模式两类。首先采用形状参量和关系参量描述网眼,然后,基于决策树C4.5算法分别对5参量描述和3参量描述数据构造分类器,运用10折交叉验证获得具有说服力的结果,其Kappa值分别为0.63和0.66,正确率分别为81.7%和82.9%,置信度90%的置信区间分别为[0.785,0.846]和[0.797,0.857]。在新数据上进行了识别效果的验证,结果表明该分类器可用于网格模式的识别。

基于改进的C4.5算法的网络流量分类方法

在基于C4.5算法的网络流量分类方法中,网络流量数据量的海量性及其特征的多样性使得决策树的构建速度、分类速度成为评价网络流量分类器的重要标准。在原C4.5算法的基础上提出一种改进的信息熵的计算方法,通过减少计算函数的复杂度,提高决策树的构建速度。实验表明,基于改进后算法的分类器在达到原有分类准确率的同时,极大地缩短了决策树的构成时间。

C4.5决策树展示算法的设计

分析了现有的展示C4.5算法结果决策树的方法的不足,设计了一种利用多叉树结构的直接输出来实现决策树展示的算法。在客户关系管理(CRM)应用于电信运营的研究项目中,该算法在数据挖掘平台上得到应用,实践证明,提高了决策树分类的效率和展示的直观性。

基于C4.5决策树算法的中医胃炎实验数据分类挖掘研究

C4.5算法是决策树数据挖掘中功能相对比较全面的一种工具。实验对经过预处理后的800例中医胃炎的辨证数据病例采用C4.5算法进行分类,建立中医胃炎辨证分类决策树,并提取决策树中蕴含的分类规则。挖掘出的分类规则基本符合中医胃炎的辨证规律以及名老中医诊治胃炎的诊疗经验。