数据挖掘技术浅析

介绍了数据挖掘的特点、任务和方法。     

数据挖掘技术在客户关系管理中的研究

本文首先分析了客户关系管理中的用户信用度分析的重要性,介绍了神经网络模型并讨论了神经网络模型在客户关系管理中的应用。     

数据挖掘方法和知识发现

数据挖掘与知识发现是一个以数据库、人工智能、数理统计、可视化四大支柱技术为基础,多学科交叉、渗透、融合形成的新的交叉学科,其研究内容十分广泛。从数据挖掘与知识发现的概念开始入手,对数据挖掘技术常见的方法进行了分类讲解,同时比较了不同种方法之间的优缺点。     

《诛仙OL》中的装备合成技巧

《诛仙OL》中的装备合成,其实是游戏中的装备炼器及灌注,也就是通常大家说的精炼。那么,应用什么样的方式可以让自己的装备合成的更顺利,得到的属性更好呢？到底由此而来中的合成是不是有章可循呢？详细的了解游戏中装备的合成流程,系统的掌握装备的合成方式,是在进行装备合成前必修的重要课程,也是寻求合成技巧的第一步。[编者按]     

数据挖掘中的个人隐私和社会影响

诸如医学、个人档案管理等领域中的数据挖掘截然不同于其它领域的数据挖掘,它的一个最大的特征就是涉及到人这个主体及其隐私问题,因此有着广泛的社会影响.通过从伦理、法律和社会的限制,主体记录的处理及相关算法、数据挖掘者的责任等方面的研究,提出私有数据挖掘中的个人隐私和社会影响问题及其解决办法.     

数据仓库与数据挖掘技术

数据挖掘是一新兴的技术,近年对其研究正在蓬勃开展.本文阐述了数据仓库及数据挖掘的相关概念,做了相应的分析,同时共同探讨了两者共同发展的关系,并对数据仓库与挖掘技术结合应用的发展做了展望.     

数据仓库与数据挖掘技术

数据挖掘是一新兴的技术,近年对其研究正在蓬勃开展。本文阐述了数据仓库及数据挖掘的相关概念,做了相应的分析,同时共同探讨了两者共同发展的关系,并对数据仓库与挖掘技术结合应用的发展做了展望。     

基于WebGIS的煤矿采掘衔接管理系统的设计与实现

分析了煤矿采掘衔接的特点与采掘衔接管理的重要性,设计了一种基于WebGIS的采掘衔接数字化管理系统,介绍了系统架构、具体实现及其功能。该系统将采掘计划、生产过程工作面的衔接、产量分析预测有机地结合起来,实现了煤矿资源的有机整合,最大限度地保护了矿井现有资源,为实现煤炭企业生产计划与实际生产的有效控制、运行和优化管理提供了整体的解决方案。     

基于时态数据库的Web数据周期规律的采掘

拟周期性能描述对象在生命周期中重复性的趋势和走向,并能忽略时间轴上不规则的伸缩和幅度上的干扰。该文以基于Ｈｂａｓｅ分史制的Ｗｅｂ数据拟周期采掘任务为背景,提出了属性趋势、趋势惯量和峰谷链、抗干扰的惯性趋势算法和峰谷算法,对拟周期采掘给出一种解决方法,通过在一组地震数据上的采掘测试表明,算法有实用价值和可接受的效率。     

Algorithm for Extracting Loosely Structured Data Records Through Digging Strict Patterns

Extracting loosely structured data records (LSDRs) has wide applications in many domains, such as forum pattern recognition, Weblogs data analysis, and books and news review analysis. Yet currently existing methods only work well for strongly structured data records (SDRs). In this paper, we propose to address the problem of extracting LSDRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the LSDRs, and propose a new algorithm to extract the Data Records (DRs) automatically. The experimental results demonstrate that our algorithm is able to effectively extract LSDRs with higher precision and recall.     

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets’ vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition (COADL-FDIAR) model for the IoT environment. The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment. To accomplish this, the COADL-FDIAR model initially pre-processes the input data and selects the features with the help of the Chi-square test. To detect and classify false data injection attacks, the Stacked Long Short-Term Memory (SLSTM) model is exploited in this study. Finally, the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency. The proposed COADL-FDIAR model was experimentally validated using a standard dataset, and the outcomes were scrutinized under distinct aspects. The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.     

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the DenseNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.     

基于本体的Deep Web数据标注

借鉴语义Web领域中深度标注的思想,提出了一种对Web数据库查询结果进行语义标注的方法.为了获得完整且一致的标注结果,将领域本体作为Web数据库遵循的全局模式引入到查询结果语义标注过程中.对查询接口及查询结果特征进行详细分析,并采用查询条件重置的策略,从而确定查询结果数据的语义标记.通过对多个不同领域Web数据库的测试,在具有领域本体支持的条件下,该方法能够对Web数据库查询结果添加正确的语义标记,从而验证了该方法的有效性.     

基于噪声数据与干净数据的深度置信网络

建立以受限玻尔兹曼机（restricted Boltzmann machine，简称RBM）为基石的深度网络模型，是深度学习研究的热点领域之一.Point-wise Gated受限玻尔兹曼机（point-wise gated RBM，简称pgRBM）是一种RBM的变种算法.该算法能够在含噪声的数据中自适应地找到数据中与分类有关的部分，从而实现较好的分类结果.假设一组数据中有噪声数据和干净数据，如何应用不含噪声的数据提升pgRBM的性能，是一个重要的研究问题.针对这一问题，首先，在传统的pgRBM基础上提出一种基于随机噪声数据与干净数据的Point-wise Gated受限玻尔兹曼机（pgRBM based on random noisy data and clean data，简称pgrncRBM）方法，其网络中与分类有关权值的初值是通过不含噪声的数据学习得到的，所以pgrncRBM在处理随机噪声数据时可以学习到更为"干净"的数据.在pgrncRBM中，与分类有关的数据与噪声都是使用RBM建模.如果噪声是图片，pgrncRBM就不能很好地去除噪声.Spike-and-Slab RBM（ssRBM）是一种处理实值数据的RBM变种模型，其定义两种不同类型的隐层用来学习实值数据的分布特性.因此，将ssRBM与pgRBM相结合，提出一种基于图像噪声数据与干净数据的Point-wise Gated受限玻尔兹曼机（pgRBM based on image noisy data and clean data，简称pgincRBM）方法.该方法使用ssRBM对噪声建模，其在处理图像噪声数据时可以学习到更为"干净"的数据.然后，通过堆叠pgrncRBM、pgincRBM和传统的RBM构建出深度网络模型，并探讨了权值不确定性方法在提出网络模型中的可行性.最后，在含噪声的手写数据集上进行MATLAB仿真实验.实验结果表明，pgrncRBM和pgincRBM都是有效的神经网络学习方法.     

Threat Modeling: Diving into the Deep End

Optimizing the working relationship between a company's IT security (ITS) group and its internal business customers is difficult at best. Who is responsible for security? What does "responsible" mean? For that matter, what does "security" mean? If ITS is solely responsible for security, as is often the case, then everything across the board will likely receive the same level of protection. In their defense, the members of ITS often don't know which asset means the most to the business, so the safest approach is to protect everything as much as possible.     

Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.     

Deep Web集成系统中同类主题数据源选择方法*

提出了一个同类主题的Deep Web 数据源选择方法,该方法通过数据源差异性分析可有效判断出新数据源的内容与集成系统中已有内容的重复度,进而利用查准率和查全率建立质量估计模型评估各数据源的质量,削弱了已有研究中因查准率低对质量评估产生的负面影响。在主流图书类网站上的实验结果表明,该方法能减少系统的负担,同时获取质量较高的同类主题的数据源。     

基于树及索引的HTML表格数据挖掘算法研究

提出了一种基于树及索引结构的HTML解析与表格数据抽取的算法,并对各子算法复杂性进行了讨论,对HTML标签存贮模型及表格数据挖掘模型进行了详细的说明,对算法所涉及的二叉树、栈、容器、递归等算法及数据结构作了清晰阐述。     

数据挖掘的定性映射模型及其在桥吊监测中的应用

属性量-质转化关系或规律，可归结为一个定性映射(Qualitative Mapping,QM)，它诱导出一个等价关系：E-R(QM)。在本文中，我们运用属性论中的定性映射方法来解决桥吊的状态监测问题。     

基于树及索引的HTML表格数据挖掘算法研究

提出了一种基于树及索引结构的HTML解析与表格数据抽取的算法,并对各子算法复杂性进行了讨论,对HTML标签存贮模型及表格数据挖掘模型进行了详细的说明,对算法所涉及的二叉树、栈、容器、递归等算法及数据结构作了清晰阐述。