基于自适应进化神经网络算法的入侵检测

针对目前多数入侵检测系统的低检测率问题,提出一种自适应进化神经网络算法AENNA。基于遗传算法和BP神经网络算法,利用模拟退火算法的概率突跳和局部搜索强的特性对遗传算法进行改进,采用双种群策略的遗传进化规则实现BP神经网络权值和结构的双重优化;通过对遗传算法的交叉算子与变异算子的改进,设计一种自适应的神经网络训练方法。实验结果表明,基于AENNA的入侵检测方法能够有效提高系统的检测率并降低误报率。     

遗传算法在数据挖掘中的应用

论文以图书发行网络管理系统为例提出了一种基于遗传算法的分类规则数据挖掘算法。通过定义适应值函数、双种群结构、遗传算子(自适应地改变交叉和变异概率)等手段的应用,进行群体间个体的评优直至最后输出最优规则集。     

改进遗传算法在求解TSP问题上的应用研究

遗传算法是一种比较成熟的智能算法,一般通过改进遗传算法的算子达到提高算法性能的目的。提出一种改进的遗传算法,遗传算子是基于近邻选择策略设计的,另外还对评估函数、种群多样性以及保留精英算子等方面对遗传算法进行了改进,并将其应用到旅行商问题的求解上,实验结果表明提出的算法是有效的。     

基于排挤遗传算法的入侵检测方法

传统遗传算法在入侵检测系统中构造的规则单一,导致检测率低,为了构造更加精确和完备的入侵规则,提出了一种基于确定性排挤遗传的规则构造算法,该算法使用确定性排挤来产生下一代种群,能够有效保持种群多样性,获得全部最优解。给出了算法的步骤和仿真,以网络数据集KDDCup99为对象,详细分析了利用该算法来生成入侵规则的具体实现过程,对染色体编码和适应度函数进行了设计和实现。最后通过实验证明了此算法的有效性,可以较好地获得入侵检测规则。     

基于父个体相似度的自适应遗传算法

标准遗传算法在产生后代个体时采用先交叉后变异的策略,一方面当父个体非常相似时,交叉操作很难产生新的个体,影响算法对新的解空间进行搜索,从而导致种群多样性的丧失;另一方面交叉产生的优秀个体再历经变异,极有可能遭破坏而影响算法的收敛性。该文根据染色体的相似性,给出了个体相似度的概念,并在此基础上提出了依据父个体相似度的大小自适应地选择遗传算子(交叉或变异)的遗传算法。仿真实验表明,与采用常规遗传策略的遗传算法相比,新算法能显著提高解的质量和收敛速度。     

协同进化算法在关联规则挖掘中的应用

文中采用了一种协同进化算法,分别利用改进的遗传算法和粒子群算法对两个种群同时进行迭代,并在种群之间引入一种信息交互机制,使两个种群协同进化。文中最后通过实验对该协同进化算法、传统的遗传算法以及粒子群算法应用于关联规则挖掘时的性能进行比较,证明了该协同进化算法在可接受的时间复杂度前提下,不仅继承了传统遗传算法挖掘关联规则时无须产生规模庞大的候选项集和有效减少扫描数据库次数的优点,更弥补了其容易早熟收敛的缺陷,从而能高效地搜索出数据库中高质量的关联规则,这点在其应用于高维数据集时尤为显著。     

改进蚁群遗传算法在大规模车间调度中的研究

本章提出一种基于相似度和种群熵的改进蚁群遗传算法,用于求解大规模调度问题。该算法将种群分成勘探、开发、勘探开发以及保留四类种群,根据各种群的功能,采用不同的遗传参数和不同进化策略。在算法中,本文用相似度来建立种群熵。最直观的反映种群的多样性,利用与种群熵相关的种群因子动态的调节种群的规模,以保证种群的多样性。加强算法的并行性。     

基于遗传算法的关联规则挖掘方法

基于对数据挖掘,特别是关联规则挖掘的研究,提出了一种改进型遗传算法的关联规则提取算法,并从编码方法、适应度函数的构造和遗传算子的设计方面进行讨论分析。通过对早熟问题的分析并改进自适应算子,提高了算法的效率,使算法在相对稳定的动态种群规模中寻找优质解。     

遗传聚类算法改进及其仿真

针对k-means算法与遗传聚类算法结合时,在其运行过程中算法易于早熟的现象,给出了一种改进的基于遗传进化的聚类算法,将k-means算法与遗传算法二者结合,并通过双种群混合方式构造初始种群,而两种群的不同来源于其各自个体构造方式的不同.适当设计遗传算子作用种群后,于进化群体中选择最优个体,以其对应各分量作为k-means算法的初始中心点,并由该个体执行k-means操作达到数据源的最终聚类之目的.仿真实验表明,该算法是一个有效的遗传聚类方法.     

一种基于GEP的分类规则挖掘算法

基于一种新的自动程序设计方法基因表达式程序设计（GEP），通过设计适应函数、初始化群体的优化、增加新的遗传算子以及采用演化策略中的（λ+μ）淘汰策略等对原始GEP算法进行有效的改进，设计出一种新的数据挖掘算法。采用UCI机器学习知识库中的数据集对该算法进行了实验，并通过与C4.5及文献[3]的比较，检验了该算法的准确性。     

A hybrid of sequential rules and collaborative filtering for product recommendation

Customers’ purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers’ purchase behavior may vary over time. In contrast, the sequential rule-based recommendation method analyzes customers’ purchase behavior over time to extract sequential rules in the form: purchase behavior in previous periods ⇒ purchase behavior in the current period. If a target customer’s purchase behavior history is similar to the conditional part of the rule, then his/her purchase behavior in the current period is deemed to be the consequent part of the rule. Although the sequential rule method considers the sequence of customers’ purchase behavior over time, it does not utilize the target customer’s purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based KNN-CF method. The proposed method uses customers’ RFM (Recency, Frequency, and Monetary) values to cluster customers into groups with similar RFM values. For each group of customers, sequential rules are extracted from the purchase sequences of that group to make recommendations. Meanwhile, the segmentation-based KNN-CF method provides recommendations based on the target customer’s purchase data for the current period. Then, the results of the two methods are combined to make final recommendations. Experiment results show that the hybrid method outperforms traditional CF methods.     

基于客户喜好的双向个性化推荐算法

为了解决现有推荐算法仅考虑同类产品间单向推荐所缺乏的灵活性,提升产品的销量及用户的购物体验,提出一种基于客户喜好的双向个性化推荐算法,不仅可以为客户精准推荐产品,还可以为商家推荐潜在客户.首先,基于产品购买网络中客户及其邻居的购买信息,扩展客户购买信息;其次设计客户产品喜好权重计算办法,分析客户的购买喜好,并在客户喜好的指导下为客户提供个性化的产品推荐;最后,基于商家提供的样本客户,挖掘与样本客户相似的客户构成社区,为商家提供潜在客户推荐以及精准客户维护.在真实数据集上的实验验证了算法的有效性.该算法从客户和商家两个维度出发实现了产品与客户的双向推荐,为个性化推荐领域的研究提供有益的帮助.     

一种基于粗糙集的特征选取算法及应用

特征选取技术主要目的之一是选取代表问题域的最优特征子集,提高分类的有效性和可伸缩性。文章在粗糙集理论框架下,提出了一种能处理不相容数据的特征选取算法,它以属性重要性和平均规则支持度作为特征选取的启发式信息,并且通过实验验证该算法的有效性。最后,将该算法应用到客户满意度特征选取中,对于识别和保留客户具有重要现实意义。     

基于复杂属性相似度的聚类算法及其应用研究

针对电信客户的有效细分问题,利用属性相似度度量思想,提出了一种面向复杂属性的聚类算法。该算法用复杂属性分布相似度函数衡量对象的相似性,然后根据相似性建立图模型,最后对图进行分割进行聚类。相比于传统基于选维和降维的聚类分析算法,提出的算法能有效处理高维数据和复杂属性。同时,算法在参数调节时,不需遍历原始数据,也减少了人工干预。利用真实电信客户数据进行的模拟实验也表明,提出的算法具有良好性能,可以有效解决电信客户细分问题。     

基于用户偏好的社会网络二级人脉推荐研究

社会网络服务(SNS)用户的人脉关系研究大多采用图论的知识,对社会网络关系图的结点和边进行探讨,而没有考虑到用户自身的偏好.因此提出一种基于用户偏好的二级人脉推荐方法.利用最小均方误差(LMS)算法,把用户偏好合理地转化为用户偏好特征向量,用相似度度量方法来计算用户之间的相似度,以确定与用户偏好最相近的用户集,并完成用户的二级好友推荐.实验结果表明,该算法的好友推荐准确度较高.     

A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization

Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization. By properly grouping those customers with similar characteristics, logistics operators are able to reduce operational costs and improve customer satisfaction levels. However, due to the heterogeneity and high-dimension of customers’ characteristics, the customer clustering problem has not been widely studied. This paper presents a fuzzy-based customer clustering algorithm with a hierarchical analysis structure to address this issue. Customers’ characteristics are represented using linguistic variables under major and minor criteria, and then, fuzzy integration method is used to map the sub-criteria into the higher hierarchical criteria based on the trapezoidal fuzzy numbers. A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is developed to group the customers into multiple clusters. The clustering validity index is designed to evaluate the effectiveness of the proposed algorithm and find the optimal clustering solution. Results from a case study in Anshun, China reveal that the proposed approach outperforms the other three prevailing algorithms to resolve the customer clustering problem. The proposed approach also demonstrates its capability of capturing the similarity and distinguishing the difference among customers. The tentative clustered regions, determined by five decision makers in Anshun City, are used to evaluate the effectiveness of the proposed approach. The validation results indicate that the clustered results from the proposed method match the actual clustered regions from the real world well. The proposed algorithm can be readily implemented in practice to help the logistics operators reduce operational costs and improve customer satisfaction levels. In addition, the proposed algorithm is potential to apply in other research domains.     

一种基于群体智能的客户行为分析算法

提出了一种基于群体智能的客户行为分析算法．首先将客户的消费模式作为平面上的一个点随机分布于平面区域内；然后依据基于群体智能的聚类方法，选用由小到大的群体相似系数进行聚类分析；最后，在平面区域内采用递归算法收集聚类结果，获得不同消费特征的客户群体．文中还提出了算法的并行策略，提高了算法对大数据量的适应性．该文以电信移动客户话费数据作为实验数据，并将算法结果与其它经典聚类算法的结果进行比较分析．分析结果表明：这种基于群体智能的客户行为分析算法能够满足客户聚类和分类的要求，特别是在大客户分析及一对一营销中特别客户的分析方面该算法有直观、类别特征明显等特点．     

The determinants of online customer ratings: a combined domain ontology and topic text analytics approach

Merchants, as well as customers, have noticed the importance of online product reviews and numeric ratings in electronic commerce websites. It is valuable if merchants can discover some potential customer value from the sheer volume of data. This paper contributes a semantic text analytics approach that can dig out the customers’ most basic concerns about their online purchase choices. More specifically, based on the hypothesis that the product reviews and overall ratings estimated by same person in a tiny time interval have a great relevance, we dexterously utilize this relevance to realize the embedded customer value. In the proposed method, take the single lens reflex camera for example, an innovative aspect extraction method that comprehensively considers the product ontology and results of the topic modeling method latent Dirichlet allocation is applied. As a result, 8 specific aspects are identified from the experimental results. For each aspect, a self-contained review feature corpus is created as an extension of some seed terms. After aspect-based sentence segmentation and context-sensitive sentiments preprocessing, aspect-oriented sentiment analysis is applied. Multiple regression analysis is then used as a statistical measure to discover determinant aspects of overall ratings. The results reveal that cost performance, image quality and product integrity are the three most influential aspects. The practical implication of our research is that merchants can efficiently modify their products, to satisfy more customers and also boost sales performance.     

基于知识库的客户网购意向预测系统

电子商务网站所产生的海量客户网购行为数据中蕴含着丰富的反映客户网购行为规律的知识。这些知识是进行客户网购意向预测以及在此基础上进一步进行商品推荐的重要依据。基于Hadoop框架构建了“客户购买意向预测系统”,实现了利用隐含在交易数据中的知识对正在浏览中的电商客户的购买意向预测。在基于知识图谱技术构建网购客户购买行为知识库的基础上,利用FP-Growth算法和数据统计方法实现了以增量方式从购买者的交易数据中获取反映用户购买规律的知识,并将取得的知识融合到知识库中。系统能够利用存储于知识库中的知识在客户进行网上购物的过程中对其购买意向进行分析和预测,从而达到提高电子商务平台的运营效率,改善客户购物体验的目的。     

An integrated data mining and behavioral scoring model for analyzing bank customers

Analyzing bank databases for customer behavior management is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. This study proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map neural network was used to identify groups of customers based on repayment behavior and recency, frequency, monetary behavioral scoring predicators. It also classified bank customers into three major profitable groups of customers. The resulting groups of customers were then profiled by customer's feature attributes determined using an Apriori association rule inducer. This study demonstrates that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development.