一种电子商务网站评价知识规则挖掘方法

电子商务网站评价知识规则是对电子商务网站的运行情况和工作质量进行评价的重要依据,优质、合理的知识规则将使评价更加公正、更加客观。在分析并建立电子商务网站评价指标体系的基础上,将一种改进的遗传算法用于电子商务网站评价的知识规则挖掘,提出了一种基于遗传算法的电子商务网站评价知识规则挖掘方法。该方法利用选择算子、助长算子、交叉算子和变异算子来产生新的知识规则,使用正确度、覆盖度和可信度来对知识规则进行评价。实例表明,这种方法在进行知识规则挖掘时是完全可行的和有效的。     

基于增量式遗传算法的分类规则挖掘

分类知识发现是数据挖掘的一项重要任务,目前研究各种高性能和高可扩展性的分类算法是数据挖掘面临的主要问题之一。将遗传算法与分类规则挖掘问题相结合,提出了一种基于遗传算法的增量式的分类规则挖掘方法,并通过实例证明了该方法的有效性。此外,还提出了一种分类规则约简方法,使挖掘的结果更简洁、更易理解。     

基于遗传算法的一阶规则获取

基于一阶谓词逻辑的一阶规则挖掘方法可处理多表挖掘且具有强的知识表达能力,成为数据挖掘(DM)技术中一种渐受重视的新方法。为了解决现有方法规则获取的性能瓶颈问题,该文提出了一种新的基于遗传算法的一阶规则挖掘算法(GILP)。实验结果表明,GILP算法能有效地挖掘一阶规则。     

基于半空间和GA的关联规则快速挖掘算法

提出了一种利用半空间模型和遗传算法(GA)对关联规则进行快速挖掘的方法。传统关联规则挖掘算法往往受到数据类型、关联规则的实际意义等约束,大大限制了知识获取的能力。而此方法不再受到上述限制的困扰,并且可以挖掘出用户感兴趣的规则,尤其对于大规模样本集的效果也是相当不错的。     

基于并行遗传算法的规则发现研究

阐述了传统遗传算法的基本思想、原理和步骤及其在数据挖掘(规则集发现)中的应用,给出了基于遗传算法的知识规则挖掘算法的基本思想和关键问题,包括知识规则表示、适应度函数定义等,继而提出多种群并行进化结构,利用精英重组策略,产生池进化模型以及自适应参数的手段调整并行遗传算法进行数据挖掘。在算法具体实现过程中,采用了动态变异交叉概率等方法,有效避免了并行遗传算法中早熟现象的发生。以北美香菇数据为例,进行并行遗传算法挖掘分类规则,实验说明了该算法在发现和进化规则方面的有效性。     

遗传算法在关联规则挖掘中的应用

数据挖掘是关联规则中一个重要的研究方向。该文对关联规则的数据挖掘和遗传算法进行了概述,提出了一种改进型遗传算法的关联规则提取算法。最后结合实例给出了用遗传算法进行关联规则的挖掘方法。     

遗传算法在关联规则挖掘中的应用

数据挖掘是关联规则中一个重要的研究方向。该文对关联规则的数据挖掘和遗传算法进行了概述,提出了一种改进型遗传算法的关联规则提取算法。最后结合实例给出了用遗传算法进行关联规则的挖掘方法。     

基于模拟退火遗传算法的关联规则挖掘

将模拟退火遗传算法加以改进,应用于关联规则挖掘,提出一种新的基于改进的模拟退火遗传算法的关联规则挖掘算法,并在该算法中,采用自适应方式动态选取交叉和变异概率,有效地抑制了早熟收敛现象,实验结果显示该方法能高效地解决关联规则挖掘问题。     

一种基于遗传算法的关联规则挖掘方法

根据关联规则挖掘的要求与特点,结合遗传算法的思想,提出了一个基于遗传算法的关联规则挖掘方法,并通过实例分析,说明是一种具有实用价值的方法。     

基于并行遗传算法的规则发现研究

阐述了传统遗传算法的基本思想、原理和步骤及其在数据挖掘(规则集发现)中的应用,给出了基于遗传算法的知识规则挖掘算法的基本思想和关键问题,包括知识规则表示、适应度函数定义等,继而提出多种群并行进化结构,利用精英重组策略,产生池进化模型以及自适应参数的手段调整并行遗传算法进行数据挖掘.在算法具体实现过程中,采用了动态变异交叉概率等方法,有效避免了并行遗传算法中早熟现象的发生.以北美香菇数据为例,进行并行遗传算法挖掘分类规则,实验说明了该算法在发现和进化规则方面的有效性.     

基于文化免疫克隆算法的关联规则挖掘研究

针对关联规则挖掘问题,给出一种基于文化免疫克隆算法的关联规则挖掘方法,该方法将免疫克隆算法嵌入到文化算法的框架中,采用双层进化机制,利用免疫克隆算法的智能搜索能力和文化算法信念空间形成的公共认知信念的引导挖掘规则。该方法重新给出了文化算法中状况知识和历史知识的描述,设计了一种变异算子,能够自适应调节变异尺度,提高免疫克隆算法全局搜索能力。实验表明,该算法的运行速度和所得关联规则的准确率优于免疫克隆算法。     

融合遗传算法和关联规则的数据挖掘方法改进

提出了一种融合改进遗传算法(Genetic algorithm, GA)和关联规则的数据挖掘方法,首先将GA交叉算子和变异算子进行自适应改进,使其在迭代过程中能够根据函数适应度值自适应调节;然后将改进后的自适应GA融入到关联规则中,充分利用GA良好的全局搜索能力,提高处理海量数据关联规则的挖掘效率。为了避免无用规则,减少不相关性的存在,在此基础上融入亲密度以提高关联规则的可靠性。在Hadoop大数据平台上通过分析交通数据验证优化后的算法,与传统方法相比,该方法提高了算法的收敛速度和鲁棒性。     

A hybrid coevolutionary algorithm for designing fuzzy classifiers

Rule learning is one of the most common tasks in knowledge discovery. In this paper, we investigate the induction of fuzzy classification rules for data mining purposes, and propose a hybrid genetic algorithm for learning approximate fuzzy rules. A novel niching method is employed to promote coevolution within the population, which enables the algorithm to discover multiple rules by means of a coevolutionary scheme in a single run. In order to improve the quality of the learned rules, a local search method was devised to perform fine-tuning on the offspring generated by genetic operators in each generation. After the GA terminates, a fuzzy classifier is built by extracting a rule set from the final population. The proposed algorithm was tested on datasets from the UCI repository, and the experimental results verify its validity in learning rule sets and comparative advantage over conventional methods.     

基于日志挖掘的Web service安全关联规则挖掘算法及应用

针对传统的Web service安全性测试方法存在的低效性和盲目性,提出了一种基于Web service日志挖掘的安全关联规则挖掘算法,并阐述了算法的应用环境。通过该算法挖掘出正常行为的关联规则,采用错误注入的方式对Web service注入预先设计的构造算子,并把执行后的日志与关联规则进行比较,进而发现Web service存在的安全性问题。实验结果表明,该算法较大地提高了日志挖掘的效率及覆盖率,同时应用该算法能较好地检测出Web service的安全性问题,进一步表明提出的算法是可行有效的。     

A statistical framework for mining substitution rules

In this paper, a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of some items with that of others. The mining of substitution rules in a transaction database, the same as that of association rules, will lead to very valuable knowledge in various aspects, including market prediction, user behaviour analysis and decision support. The process of mining substitution rules can be decomposed into two procedures. The first procedure is to identify concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second procedure is then on the substitution rule generation. In this paper, we first derive theoretical properties for the model of substitution rule mining and devise a technique on the induction of positive itemset supports to improve the efficiency of support counting for negative itemsets. Then, in light of these properties, the SRM (substitution rule mining) algorithm is designed and implemented to discover the substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of the SRM algorithm proposed. It is shown that the SRM algorithm not only has very good execution efficiency but also produces substitution rules of very high quality.     

Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier. Our approach is mainly composed of two phases. First, a large number of candidate rules are generated for each class using fuzzy association rules mining, and they are pre-screened using two rule evaluation criteria in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the classes specified in IIDS: namely Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L-remote to local. During the next stage, boosting genetic algorithm is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. Boosting mechanism evaluates the weight of each data item to help the rule extraction mechanism focus more on data having relatively more weight, i.e., uncovered less by the rules extracted until the current iteration. Each extracted fuzzy rule is assigned a weight. Weighted fuzzy rules in each class are aggregated to find the vote of each class label for each data item.     

基于改进多层次模糊关联规则的定量数据挖掘算法

针对单一层次结构实现规则提取具有规则提取准确性不高、算法运行时间长、难以满足用户使用需求的问题,提出一种基于改进多层次模糊关联规则的定量数据挖掘算法。采用高频项目集合,通过不断深化迭代的方法形成自顶向下的挖掘过程,整合模糊集合理论、数据挖掘算法以及多层次分类技术,从事务数据集中寻找模糊关联规则,挖掘出储存在多层次结构事务数据库中定量值信息的隐含知识,实现用户的定制化信息挖掘需求。实验结果表明,提出的数据挖掘算法在挖掘精度和运算时间方面相较于其他算法具有突出优势,可为多层次关联规则提取方法的实际应用带来新的发展空间。     

Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard sate in coal mines. Due to imbalanced distribution of examples describing states “hazardous” and “safe”, the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters‘ values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier’s quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.     

A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.