大倾角俯斜综采过陷落柱技术研究与实践

大倾角俯斜开采一直制约着煤矿综合机械化的发展。在实现缓倾斜煤层开采的基础上,进一步探索和分析了25°～30°大倾角俯斜综采工作面的回采工艺、技术研究及其可行性,为今后类似条件下工作面的开采提供理论依据和生产实践指导,进而创造更大的经济效益和社会效益。     

东山煤矿“三下采煤”应用经验

概述了东山煤矿“三下采煤”技术发展现状、工艺流程、技术要求及发展过程中遇到的难点问题,并对“三下采煤”技术给东山煤矿带来的经济效益和社会效益进行了分析。     

Understanding user intent on the web through interaction mining

Predicting the goals of internet users can be extremely useful in e-commerce, online entertainment, and many other internet-based applications. One of the crucial steps to achieve this is to classify internet queries based on available features, such as contextual information, keywords and their semantic relationships. Beyond these methods, in this paper we propose to mine user interaction activities to predict the intent of the user during a navigation session. However, since in practice it is necessary to use a suitable mix of all such methods, it is important to exploit all the mentioned features in order to properly classify users based on their common intents. To this end, we have performed several experiments aiming to empirically derive a suitable classifier based on the mentioned features.     

A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification

High-utility itemsets mining (HUIM) is a critical issue which concerns not only the occurrence frequencies of itemsets in association-rule mining (ARM), but also the factors of quantity and profit in real-life applications. Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database. Discovered HUIs may become invalid or new HUIs may arise when transactions are inserted, deleted or modified. Existing approaches are required to re-process the updated database and re-mine HUIs each time, as previously discovered HUIs are not maintained. Previously, a pre-large concept was proposed to efficiently maintain and update the discovered information in ARM, which cannot be directly applied into HUIM. In this paper, a maintenance (PRE-HUI-MOD) algorithm with transaction modification based on a new pre-large strategy is presented to efficiently maintain and update the discovered HUIs. When the transactions are consequentially modified from the original database, the discovered information is divided into three parts with nine cases. A specific procedure is then performed to maintain and update the discovered information for each case. Based on the designed PRE-HUI-MOD algorithm, it is unnecessary to rescan original database until the accumulative total utility of the modified transactions achieves the designed safety bound, which can greatly reduce the computations of multiple database scans when compared to the batch-mode approaches.     

Graphical causal inference and copula regression model for apple keywords by text mining

Apple is a leading company of technological evolution and innovation. This company founded and produced the Apple I computer in 1976. Since then, based on its innovative technologies, Apple has launched creative and innovative products and services such as the iPod, iTunes, the iPhone, the Apple app store, and the iPad. In many fields of academia and business, diverse studies of Apple’s technological innovation strategy have been performed. In this paper, we analyze Apple’s patents to better understand its technological innovation. We collected all applied patents by Apple until now, and applied statistics and text mining for patent analysis. By using graphical causal inference method, we created the causal relations among Apple keywords preprocessed by text mining, and then we carried out the semiparametric Gaussian copula regression model to see how the target response keyword and the predictor keywords are relating to each other. Furthermore, Gaussian copula partial correlation was applied to Apple keywords to find out the detailed dependence structure. By performing these methods, this paper shows the technological trends and relations between Apple’s technologies. This research could make contributions in finding vacant technology areas and central technologies for Apple’s R&D planning.     

Fuzzy utility mining with upper-bound measure

Fuzzy utility mining has been an emerging research issue because of its simplicity and comprehensibility. Different from traditional fuzzy data mining, fuzzy utility mining considers not only quantities of items in transactions but also their profits for deriving high fuzzy utility itemsets. In this paper, we introduce a new fuzzy utility measure with the fuzzy minimum operator to evaluate the fuzzy utilities of itemsets. Besides, an effective fuzzy utility upper-bound model based on the proposed measure is designed to provide the downward-closure property in fuzzy sets, thus reducing the search space of finding high fuzzy utility itemsets. A two-phase fuzzy utility mining algorithm, named TPFU, is also proposed and described for solving the problem of fuzzy utility mining. At last, the experimental results on both synthetic and real datasets show that the proposed algorithm has good performance.     

数据挖掘技术在教育学中的应用

随着信息化时代的不断发展,数据挖掘技术日趋成熟,满足了人们对于大量信息的处理要求。目前,数据挖掘技术已经越来越多的应用于金融、通讯和交通等各行各业,但是在教育领域的应用相对较少。针对这一现状,在传统的分析方法上采用了关联规则挖掘,聚类挖掘等多种算法,对青少年同伴关系、人际关系和网络成瘾等各方面进行研究,得到不同性别的青少年同伴亲疏程度不同等一系列结论,提出了数据挖掘技术在教育学领域的应用的新前景。     

浅谈Web数据挖掘技术

随着网络的快速发展与普及,大量有用的网络信息给人们生活、工作和学习带来了便利。与此同时网络中还存在着许多无用的信息,如何从浩如烟海的数据海洋中,快速准确的查找数据,成为了当今社会不可忽视的问题。Web数据挖掘技术,正是解决这一问题的关键。该文从Web数据挖掘技术的角度,阐述Web数据挖掘的概念、分类、过程及常见的Web数据挖掘算法。     

数据挖掘常用分类算法研究

数据库、数据仓库以及其他存储信息库中潜藏着很多与商业、科学研究等活动的决策有关的数据和知识。对于数据挖掘中的数据分析,通常有两种常见的方法,即分类和预测,首先对数据库中的数据进行分类归纳,然后根据分类规则可以得到比较有价值的数据,然后我们可以根据这个数据来预测得到一些包含未来趋势的信息。在常见的分类算法中,决策树算法是一个有着很好扩展性的算法,可以应用到大型数据库中,可以对多种数据类型进行处理,分类模式容易转化为分类规则,结果也十分的浅显易懂易于理解。该文主要先介绍了几种常用的分类算法,然后具体介绍决策树算法的过程以及在分类算法实际应用中的优缺点。     

CNGI青海湖系统建设中的若干技术问题

CNGI 青海湖系统支撑青海湖区域的生态保护和多学科交叉融合的科研协作。针对青海湖基础设施种类众多的特点,我们发展了 IPv6 相关关键技术,应用于基础网络环境建设、数据传输、设备管理与状态监控等多个层面,并充分考虑了由此带来的安全隐患,制定了相应安全策略。这些技术可以支持多种传感器构成的 IPv6 网络,并支持 IPv6 的相关应用软件研发,促进大规模、跨领域复杂科学问题研究工作。