基于Oracle DataMining的数据挖掘在石化企业的应用

随着数据挖掘技术的研究与开发,数据挖掘技术目前主要应用于智能商务领域,在石化领域中如何展开数据挖掘开发应用还有待研究。基于Oracle Data Mining(ODB)数据挖掘工具,结合实例介绍了在石化企业中应用数据挖掘的系统方法和基本过程。

基于DataMine的自动化品位控制建模软件功效及应用——以纳米比亚湖山铀矿为例

纳米比亚湖山铀矿的矿体结构复杂,分支复合较为常见,矿岩界线随上下岩性组合多变,采矿生产对品位控制模型的依赖较大。传统品位控制模型的构建步骤较为复杂且耗时较长,对操作人员的技术能力要求较高,明显制约采矿生产的降本增效。针对湖山铀矿的矿体和工程特征,在DataMine矿业软件平台上利用JavaScript语言开发了自动构建品位控制模型的插件。该插件通过调用DataMine内部函数,按照既定的建模技术流程和估值参数方案一键完成基础数据导入、钻孔组合、特高品位处理和样品等长组合等全部工作流程,运用最近距离法划分矿化域的功能,替代传统手工圈定矿体,可实现自动调取估值参数构建品位控制模型的功能。该插件不但简化建模步骤,显著缩短建模耗时,而且匹配矿山日常生产的实际情况和操作习惯,经过生产检验满足生产需求。

Data Layout and Scheduling Tasks in a Meteorological Cloud Environment

Meteorological model tasks require considerable meteorological basis data to support their execution.However,if the task and the mete-orological datasets are located on different clouds,that enhances the cost,execution time,and energy consumption of execution meteorological tasks.Therefore,the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations.To the best of our knowledge,this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout.First,we use the FP-Growth-M(frequent-pattern growth for meteorological model datasets)method to mine the relationship between meteorological models and datasets.Second,based on the relation,we propose a heuristics algorithm for laying out the meteorological datasets and scheduling tasks.Finally,we use simulation results to compare our proposed method with other methods.The simulation results show that our method reduces the number of involved clouds,the sizes of files from outer clouds,and the time of transmitting files.

A novel observation points‐based positive‐unlabeled learning algorithm

In this study,an observation points‐based positive‐unlabeled learning algorithm(hence called OP‐PUL)is proposed to deal with positive‐unlabeled learning(PUL)tasks by judiciously assigning highly credible labels to unlabeled samples.The proposed OP‐PUL algorithm has three components.First,an observation point classifier ensemble(OPCE)algorithm is constructed to divide unlabeled samples into two categories,which are temporary positive and permanent negative samples.Second,a temporary OPC(TOPC)is trained based on the combination of original positive samples and permanent negative samples and then the permanent positive samples that are correctly classified with TOPC are retained from the temporary positive samples.Third,a permanent OPC(POPC)is finally trained based on the combination of original positive samples,permanent positive samples and permanent negative samples.An exhaustive experimental evaluation is conducted to validate the feasibility,rationality and effectiveness of the OP‐PUL algorithm,using 30 benchmark PU data sets.Results show that(1)the OP‐PUL algorithm is stable and robust as unlabeled samples and positive samples are increased in unlabeled data sets and(2)the permanent positive samples have a consistent probability distribution with the original positive samples.Moreover,a statistical analysis reveals that POPC in the OP‐PUL algorithm can yield better PUL performances on the 30 data sets in comparison with four well‐known PUL algorithms.This demonstrates that OP‐PUL is a viable algorithm to deal with PUL tasks.

KDD的研究现状及数据相关性分析

阐述什么是KDD和DataMining,及目前国外在DataMining中研究的一部分重要内容的概况,并提出一种用于数据预处理的方法——相关性分析,它可使数据的性质与KDD的目标更加紧密的相连,并能提取出正确和容易理解的数据,用于后续的KDD分析。

基于DATAMINE的三维采矿工程可视化建模技术研究

采矿工程三维可视化模型的建立是矿山数字化的核心组成部分,对矿山设计、生产、残矿回采等具有重要意义。结合安徽凤凰山铜矿,以DATAMINE软件为平台,阐述了地下采矿工程线框模型的建立流程,分析和解决了三维建模的部分关键技术,建立了三维可视化模型。

当前国际上较为流行的矿业软件评价

就当前国际上较为流行的矿业软件中的Surpac Vision和Datamine两个软件进行了介绍和评价。分析了Surpac Vision三维可视化矿业软件及其主要功能特点,并根据该软件在进行矿床建模时的几个主要过程和模块,介绍了各模块的基本原理和功能,综合论证了该软件在矿山设计和建模方面的应用前景;介绍了Datamine软件的主要产品和应用,以及其最新产品。

基于Datamine的大厂矿体三维可视化

以广西大厂矿体为研究对象,应用Datamine构建了矿体的三维模型,通过模型能直观清楚地看到地层的空间特征,矿体的空间位置;为计算矿体储量和品位报告奠定了基础,从而为矿山合理开发利用资源提供科学依据。

凤凰山铜矿地质数据库建库技术

基于数字化软件Datamine建立凤凰山铜矿地质数据库,生成三维钻孔模型,并进行矿体圈定,实现了矿山地质信息的查询、修改、实时更新、输出自动化及可视化,为矿山的生产和设计提供了重要决策平台。

最近距离法在贵金属矿体圈定中的应用——以南非某层状铂矿为例

最近距离插值法简称最近距离法,是一种相对简单的空间差值方法,在目前流行的地质资源估算软件中都提供这方面的功能。但是最近距离法因为其固定的缺点,在资源估算实践中并不常用。圈定矿体或岩相域在传统地质建模中一般使用手工圈定,这项工作一般为地质建模和资源估算工作中耗时最长的一个步骤。本文结合南非某大型铂矿项目,探讨使用最近距离法来实现自动圈定岩相域边界,提高工作精度和效率,文中以使用Datamine Studio软件为例,介绍具体建模步骤,并应用到实际项目,获得了良好效果。

数据挖掘概念及国内外现状

随着信息技术的高速发展,人们积累的数据量急剧增长,如何从海量的数据中提取有用的知识成为当务之急。数据挖掘就是为顺应这种需要应运而生发展起来的数据处理技术。其主要任务是关联分析、分类、预测时序模式和偏差分析等。是知识发现(knowledge discovery in database)的关键步骤。数据挖掘技术是人们长期对数据库技术进行研究和开发的结果。起初各种商业数据是存储在计算机的数据库中的,然后发展到可对数据库进行查询和访问,进而发展到对数据库的即时遍历。数据挖掘使数据库技术进入了一个更高级的阶段,它不仅能对过去的数据进行查询和遍历,并且能够找出过去数据之间的潜在联系,从而促进信息的传递。

深井开采回采顺序数值模拟优化研究

采用大型矿业软件Datamine构建了冬瓜山矿的地表、岩层、矿体及块段模型;研究了Datamine块段模型与Flac3D计算模型的数据结构、耦合模式及方法,成功地将块段模型转换成数值计算模型.针对冬瓜山深井缓倾斜矿体复杂的开采技术条件,并考虑日产万吨生产能力和主要回采次序要求,设计了两种可行的回采顺序模拟方案,开展了冬瓜山深井开采回采顺序的数值模拟优化研究,分析了不同方案应力、位移和塑性区的分布规律,提出了优化的回采顺序方案.

A data mining approach to characterize road accident locations

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. Theassociation rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

薄层状矿体自动圈定矿层地质建模方法

传统的计算机矿床地质模型的建立方法是根据剖面钻孔数据资料,在剖面上人工对矿体进行圈定线圈,再在三维空间将各剖面连接成线框(Wireframe),形成三维封闭的矿体。对于特定的薄层矿体,因为矿层高程方向相对于水平方向极小,矿层薄,剖面间相对距离较大,手工方法圈定出的线圈难以在三维空间连接成三维地质模型,从而难以计算地质资源量。本文以国外某大型红土型镍矿为例,借助矿业软件Datamine Studio,探讨用地质统计学方法建立三维地质模型,以准确估算地质资源量和服务采矿设计。

A Data Mining Based Approach to Customer Behaviour in an Electronic Settings

The understanding of customer incidents and behaviour is crucial to the success of any organization. Evidence from literature shows a prediction pattern of products to customer. These studies predicted product characteristics leaving out the customers characteristics. To address this gap, this study aims to design datamining system and implement it on an electronic commerce organization website. The customer information and history (clickstreams) from the electronic commerce website was used to predict the customers’ behaviour. This will give meaningful and usable data patterns to organizations. Python programming language was used to design the datamining system, while PHP, HTML, and JavaScript were used for the e-commerce website. A brief description of the background of e-commerce and data mining, previous work of researchers who have worked on data mining in e-commerce settings, was reviewed and the relationship between their findings and this work was established. The data mining system utilizes consensus clustering technique and the clustering algorithm with a graphical-based approach. Furthermore, the interaction between the data mining system and the customer’s dataset on an ecommerce website was defined. Quantitative evidence for determining the number and membership of possible customer behavioural clusters within the dataset was generated.

Interactive Protein Data Clustering

In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.

Data Mining——统计学的新进展

随着电脑、网络技术的发展，要获取某一问题的有关资料已经不是非常困难的事情了。但是对于数量大、涉及面宽的数据，靠以往人工汇总报表是无法完成的，而那种由简单汇总、按指定模式去分析的统计方法也无法适应这类数据的分析。因此，一种智能化的、能综合应用各种统计方法来分析庞大数据资料的软件就应运而生，这就是目前国际上统计最热门的话题 Data Mining技术的市场需求和它的技术支持背景。那么，如何理解 Data Mining这一名词？其作用是什么？ Data Mining技术涉及到哪些领域？它和统计分析有什么不同？ Data Mining有哪些功能？有何具体应用？针对这些问题，为了让越来越多的读者了解并掌握 Data Mining技术，本文作者日前采访了国家统计局统计教育中心主任、中国统计教育学会副会长王吉利先生和统计界知名人士、上海财经大学张尧庭教授。