基于web挖掘的空间数据挖掘技术研究

空间数据挖掘技术(Spatial Data Mining)是建立在空间数据库的基础上,通过使用各种机器学习技术,从海量空间数据中挖掘出未知的有用的规律和知识,从而提供支持决策的依据.在现在已建立的GIS空间数据库中,大量的可分析、分类的知识,如空空位置分布规律、空间关联规则、形态特征区分规则等都隐藏在空间数据中需要被挖掘才能被发现.因此,空间数据挖掘技术就显得尤为重要.因而对于空间数据挖掘技术,特别是基于Web挖掘部分的技术进行研究.     

基于数据挖掘技术的入侵检测系统

详细论述了在入侵检测系统中数据挖掘技术的应用，对现有的应用于入侵检测的数据挖掘技术如关联规则、聚类分析、特征分类等进行了分析和综合。结合元挖掘等新概念，本文给出了一个应用数据挖掘技术的入侵检测系统IDSDM框架。该框架具有良好的智能性和自适应性。     

采用Java3D构建虚拟校园技术的研究

Java3D是一种基于场景结构图的新一代三维图形API,继承了Java语言的优点,同时具有交互性、支持多媒体和节省网络带宽等优点,具有很好的应用空间.鉴于此,在研究Java3D构建交互式三维虚拟场景的过程、方法和相关技术的基础上,以石家庄铁道学院为例,以Java和Java3D为构建平台、以网络技术和数据库技术为支持,开发了一个虚拟校园系统,用户可以在其中漫游、对虚拟场景进行交互操作和信息查询、通过聊天进行信息交流.该系统直观地展示了校园景观及设施,方便了用户对校园信息的查询,取得了良好的效果.     

包装纸盒CAD系统的Java解决方案

论述了Web环境下包装纸盒CAD系统的体系结构、系统功能的分配原则、图形显示技术、三雏可视化技术.在此基础上,综合运用Java/Java Applet/Java2D/Java3D技术对包装纸盒CAD软件进行积极的探索.     

数据挖掘技术在共混材料制备的工艺研究中的应用

介绍了在共混材料制备工艺研究中数据挖掘技术的应用,重点讨论了基于关联规则的模型建立与应用的过程,并用数据挖掘技术对可交联硅烷接枝聚乙烯共混材料制备中退火工艺与材料结晶性能的关联性进行了研究。最后讨论了数据挖掘技术在高分子材料领域未来的发展方向。     

应用Java3D技术构造网络虚拟现实环境的方法

Java3D是Java语言在三维领域扩展的一组API,可用于快速构建虚拟现实环境.通过比较两种创建虚拟现实环境的方法,提出一种利用Java3D技术创建虚拟现实环境的方法.着重介绍了利用Java3D构建虚拟现实环境的几个关键技术和具体实现方法,并实现了一个原型系统,同时给出了系统结构和功能.用户可以利用该系统非编程快速搭建所需的三维场景,并且能实现人与场景的交互.     

数据库中广义模糊关联规则的挖掘

引入了广义模糊关联规则的概念,给出挖掘规则的计算方法,用来进行数据挖掘,以找出隐藏在数据库当中那些有用的而未被发现的知识。     

4K/3D电影技术演进及新技术应用分析

近年来,4K、3D、巨幕、视觉特效、沉浸式声音等高新技术格式电影迅猛发展,云计算和大数据等新一代信息技术加速应用,从而积极推动电影产业向高新技术产业转型升级。本文阐述了4K/3D电影的技术演进和发展趋势,并基于电影云制作平台和电影大数据挖掘分析可视化平台构建,分析了云计算和大数据技术在4K/3D电影中的应用。     

分布式数据库中关联规则挖掘的研究

在数据挖掘研究中,关联规则挖掘作为数据挖掘研究中的一个重要部分,引起越来越多的关注。因此,主要研究关联规则挖掘,首先介绍关联规则挖掘的一些基础知识、概念描述等,然后对关联规则挖掘的常用算法进行分类探讨,最后分析其中的几种典型算法。     

虚拟装配序列生成及其评价系统的研究

利用X3D虚拟现实技术建立了产品装配信息模型,运用虚拟现实技术中的碰撞检测技术实现了装配序列的生成,最后通过灰关联分析方法实现了装配序列的评价．以Java为开发平台,在Java中嵌入X3D,结合包围盒层次法中的轴向包围盒AABB和方向包围盒OBB,开发出的虚拟装配序列生成及其评价软件,为装配序列的研究提供了一个基于X3D的全新平台,最后,通过实验验证了系统的可行性和有效性．     

关联规则挖掘在企业运作策略调查问卷中的应用

通过分析企业运作策略问卷调查数据,提取出不同的运作策略与绩效间的关联规则。首先介绍了关联规则以及关联规则兴趣度的度量。对于问卷数据,进行了预处理,确定挖掘的数据集及相关属性。然后应用关联规则挖掘工具,挖掘出潜在的有用规则。通过分析关联规则,找出企业运作策略及绩效间的关系,为企业提供决策支持,提高企业的竞争力。     

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

In recent years, a trend of electronic health record (EHR) system can be seen increasingly in the hospitals, which has generated huge amount of electronically stored data of patients. Association rule mining technique is very helpful in the numerous applications of healthcare (e.g., correlation between disease and symptoms, disease and offering effective treatment and predicting risks of disease based on the historical data, etc.). The data collected by an EHR system are very important for the medical research. Currently, a patient health report is derived on the basis of a physician’s own experience and on the association rule mining results of a local EHR system maintained by a particular hospital. Association rule mining results will be more accurate if the data of all local EHR systems are integrated and association rule mining is performed. Integration of local EHR systems requires the sharing of local EHR data. Sharing of patient records violates the privacy of patients. Hence, medical research is focused on the problem of mining association rules without sharing of local private EHR data. Privacy-preserving distributed association rule mining (PPDARM) solves this issue by mining the association rules while preserving the privacy of patients. In this paper, an approach for the PPDARM is proposed for collaboratively performing association rule mining by all local EHR systems while preserving the privacy. The proposed approach is also analysed with the heart disease dataset.     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

空间数据立方体的物化视图选择方法研究

针对决策支持系统(DSS)中集成空间分析能力的应用趋势，研究基于空间数据仓库的一种决策分析工具——空间在线分析处理(Spatial OLAP)，拟解决影响空间OLAP在线响应的瓶颈难点——空间数据立方体的物化问题。首先系统地提出空间OLAP的模型，然后对现有空间度量物化视图选择方法进行改进，提出了双向空间Greedy算法。实验证明．该算法在降低选择时间和求解质量两方面具有更好的表现。     

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.     

Privacy-preserving distributed mining of association rules using Elliptic-curve cryptosystem and Shamir’s secret sharing scheme

Distributed data mining has played a vital role in numerous application domains. However, it is widely observed that data mining may pose a privacy threat to individual’s sensitive information. To address privacy problem in distributed association rule mining (a data mining technique), we propose two protocols, which are securely generating global association rules in horizontally distributed databases. The first protocol uses the notion of Elliptic-curve-based Paillier cryptosystem, which helps in achieving the integrity and authenticity of the messages exchanged among involving sites over the insecure communication channel. It offers privacy of individual site’s information against the involving sites and an external adversary. However, the collusion of two sites may affect the privacy of individuals. To address this problem, we incorporate Shamir’s secret sharing scheme in the second protocol. It provides privacy by preventing colluding sites and external adversary attack. We analyse both protocols in terms of fulfilling the privacy-preserving distributed association rule mining requirements.     

Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences

Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, nonstationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a nonstationary Gaussian process model across the clusters, which allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets. Supplementary materials for this article are available online.     

从需求出发的信息可视化设计方法研究

目的在数据来源繁杂、数据量巨大的的情况下,从信息设计的角度探索提高信息可视化设计质量的方法。方法分析笔者在执行几个信息可视化设计项目过程中应用到的用户研究以及交互设计相关理论,以及具体应用这些理论的方法。对基于用户需求的可视化设计流程和执行方法进行总结。结果提出关注信息传达本身、根据应用场景来设计心理模型与信息架构、对数据进行观点挖掘这几个方面来优化设计策略。结论信息可视化应逐渐从关注技术转变到关注使用者的需求本身,这个需求才是数据成为有价值的信息的关键。     

Investigation of work zone crash casualty patterns using association rules

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40 mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.     

CARM: Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.