数据流中基于滑动窗口的最大频繁项集挖掘算法*

挖掘数据流中最大频繁项集是从数据流中获得信息的一种有效手段,是数据流挖掘研究的热点之一。结合数据流的特点,提出了一种新的基于滑动窗口的最大频繁项集挖掘算法。该算法用位图来存储数据流中流动的数据;采用直接覆盖的方法存储和更新数据流上的数据;在深度优先搜索挖掘最大频繁项集时,除采用经典的剪枝策略外,还提出了与父等价原理相对应的子等价剪枝策略;最后将挖掘结果存储在索引链表中以提高超集检测效率,进一步减少挖掘最大频繁项集的时间。理论分析和实验结果证实了该算法在时间和空间上的有效性。     

基于Web日志挖掘的客户访问兴趣分析

数据预处理是Web日志挖掘中的一项关键技术,如何分析客户访问兴趣是Web挖掘中的一个重要研究课题。概述了Web日志挖掘的概念、方法和步骤,重点分析了数据预处理的主要步骤及其实现技术,考虑客户访问路径和浏览时间长度等因素,给出并计算分析了网站用户的访问兴趣的方法。汽修之家网站日志的实际测试结果表明了该方法的有效性。     

自动化综采工作面概念探讨

介绍了国内自动化综采工作面的技术现状,并将综采工作面的设备分为生产设备、辅助设备、测控系统;针对目前业内工作面名称混乱的情况,认为"自动化综采工作面"的命名比较恰当;提出了自动化综采工作面技术可划分为综采工作面局部自动化、综采工作面半自动化、综采工作面全自动化、综采工作面智能自动化4个等级并给出了具体特征;介绍了自动化综采工作面的发展途径,指出把自动化综采工作面看成是一台可以自动行走、自动割煤、自动支护、自动装煤、自动运煤的大型机器更符合实用要求。     

某矿区变电站进线段防雷改造措施及仿真分析

介绍了某矿区110kV变电站线路防雷措施的基本情况,分析了该变电站35kV进线段遭受雷击的原因,即线路绝缘水平遭人为提高后与站内绝缘水平等级不匹配和进线段杆塔接地电阻过高,并提出了相应的采用加装绝缘子保护间隙配合避雷器进行差异性保护和采用树枝状接地装置的改造措施。ATPEMTP仿真分析结果验证了改造措施的可行性。     

差异化运动参数的有效挖掘技术仿真

研究运动参数准确挖掘方法.在运动参数挖掘的过程中,由于图像采集时间间隔比较短,短时间内运动幅度的变化非常小,所以很难对运动参数差异性进行准确的描述,无法实现运动参数准确挖掘.提出利用蚁群聚类算法的运动参数挖掘方法.根据约束模糊聚类相关原理,获取运动参数特征聚类目标函数,对特征差异目标进行最大化处理,完成运动参数特征提取.计算残留信息素对于聚类中心的隶属度,设置合理的蚁群目标搜索调整因子,计算聚类中心和对应的偏差,并对偏差进行有效补偿,完成参数挖掘.实验结果表明,利用改进算法进行运动参数挖掘,能够有效提高挖掘的准确性,提高数据管理的效率.     

基于CRF的中文评论有效性挖掘产品特征

方面级意见挖掘的任务通常包括从客户评论中抽取产品的特征、与产品特征相关联的观点词识别以及观点的极性判断三个方面。围绕如何实现中文评论的方面级意见挖掘问题,提出了利用条件随机场实现中文评论的方面级意见挖掘的四个主要步骤：数据预处理、训练集准备、为条件随机场模型定义学习函数、应用模型标注新的评论数据。在此基础上,通过以五种实际产品的中文评论语料为数据集,对该方法进行了数据实验。实验结果表明,该方法针对不同类型观点元素的抽取在评估性能指标上大部分达到或超过80%。为了进一步验证所提出方法的有效性,将研究结果进行了差异显著性检验。结果显示,用CRF对中文评论进行方面级意见挖掘和对英文评论的方面意见挖掘的性能差异不大。最后,比较了三种不同方法的方面抽取精度和情感分类精度,实验结果表明,CRF方法优于词典化的隐马尔可夫模型和关联规则挖掘方法。     

Moodle平台数据挖掘方法设计与实现

教育数据挖掘是一个新兴的研究方向。如何把存储在教育软件系统中的数据转变为有意义的信息,并为教育决策、优化教学过程服务,已成为大多数教育工作者所关注的内容。文中总结了当前教育数据挖掘的研究现状,介绍了一种基于Excel的简单数据挖掘方法。该方法利用模糊C均值聚类算法,对Moodle平台积累的日志数据进行分析,找出有相似学习行为的学生,为学习社区的小组划分和研究学习模式服务。实验表明,该方法能够更准确地对学生进行分类,而且操作更为简单、方便。     

基于网络信息安全防范与Web数据挖掘技术整合的研究

人类现在已经全面进入了网络信息时代,网络经济也出现了较为快速的发展。然而现在网络经济的进一步发展却受到了信息安全的制约,因此人们对如何保障信息安全给予了越来越多的关注。现在,通过对Web数据挖掘技术的充分运用能够促进网络信息安全的进一步提升,与此同时,其还逐渐的成为促进信息安全防范绩以及网络信息安全有效提升的关键性技术。本文针对基于网络信息安全防范与Web数据挖掘技术的整合进行了分析和探讨。     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure‐less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map‐building process and an online localization algorithm. The approach combines the strengths of visual‐based place recognition, LIDAR‐based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard‐rock mines (including a 600m‐deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state‐of‐the‐art SLAM research systems and real‐time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.