数据挖掘技术及Web应用

在计算机与信息技术普及的今天,人们的消费需求、行为和习惯都可以化为海量的用户数据。这样的数据挖掘成果,正逐步为公众所分享。     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan

Decision-making for the debris-flow management involves multiple decision-makers often with concerning geomorphological and hydraulic conditions. Spatial decision support systems (SDSS) can be developed to improve our understanding of the relations among the natural and socio-economic variables to the occurrence/non-occurrence samples of debris-flow. Accordingly, the goal of this study is to development a debris-flow decision support system to manage and monitor the debris-flows in Nan-Tou County, Taiwan. The present study, more specifically, combines a spatial information system with an advanced Data Mining technique to investigate the debris-flow problem. In the first stage, our spatial information system integrates remote sensing, DEM, and aerial photos as three different resources to generate our spatial database. Each of the geomorphological and hydraulic attributes are obtained automatically through our spatial database. Then, a Data Mining classifier (hybrid model of decision tree (D.T.) + support vector machine (S.V.M.)) will be used to analyze and resolve the classification of occurrence of debris-flow. The contribution of this study has found that watershed area and NDVI (Normalized Difference Vegetation Index) are the crucial factors governing debris-flow by means of decision tree analysis. Further, the performance of prediction accuracy on testing samples through support vector machine is 73% which could be helpful for us to have better understanding of debris-flow problem.     

基于元胞自动机的分布式数据挖掘分类器

针对常用分类方法分类精度较低和内存消耗较高的问题,设计一种基于多吸引子元胞自动机（MACA）的模式分类器tsPCM,把它应用于分布式数据挖掘。通过改变MACA的描述方法,用依赖串和依赖向量将分类过程设计成两阶段,用遗传算法优化设计。实验结果表明tsPCM具有较高的分类精度和较低的内存消耗,分类复杂度由O（n^3）降低到线性级O（n）,具有较好的应用价值。     

A rapid-prototyping framework for extracting small-scale incident-related information in microblogs: Application of multi-label classification on tweets

Small scale-incidents such as car crashes or fires occur with high frequency and in sum involve more people and consume more money than large and infrequent incidents. Therefore, the support of small-scale incident management is of high importance.Microblogs are an important source of information to support incident management as important situational information is shared, both by citizens and official sources. While microblogs are already used to address large-scale incidents detecting small-scale incident-related information was not satisfyingly possible so far.In this paper we investigate small-scale incident reporting behavior with microblogs. Based on our findings, we present an easily extensible rapid prototyping framework for information extraction of incident-related tweets. The framework enables the precise identification and extraction of information relevant for emergency management. We evaluate the rapid prototyping capabilities and usefulness of the framework by implementing the multi-label classification of tweets related to small-scale incidents. An evaluation shows that our approach is applicable for detecting multiple labels with an match rate of 84.35%.     

两种聚类算法的比较与实现

基于统计的系统聚类分析是一种重要的数据挖掘算法。研究了一种多重系统聚类模型及其算法实现,把变量聚类和样本聚类相结合,并使用了两种方法赋值样本数据阵,使聚类结果更加直观。     

基于缩进轮廓的HTML文档重复模式挖掘方法

HTML文档重复模式挖掘是找到Web页面编码模版的关键,是Web数据自动抽取和Web内容挖掘的基础。传统的基于字符串匹配和树匹配的重复模式挖掘方法虽然具有较高的精确度,但是其性能对于处理海量的Web页面来说仍然是一个挑战。为了提高性能,提出了一种基于缩进轮廓的HTML文档重复模式挖掘方法。该方法首先定义了缩进轮廓模型,是一种由HTML文档每行代码的缩进值及行首的HTML标签构成的数据结构,它是HTML文档的一种简化抽象;该方法通过检测缩进轮廓中的串联重复波段,间接地挖掘HTML文档中的重复模式。实验表明,该方法不但具有较高的精确度,而且较明显地提升了性能。     

基于数据仓库的决策支持系统研究与建设

文章首先介绍了决策支持系统（DSS）及其几点不足之处,指出新兴的数据仓库技术的固有特点可以弥补DSS的不足。在此基础上,文章提出了基于数据仓库技术的决策支持系统的体系架构,并结合实际的项目“浦东新区防灾救灾应急处理系统”阐述了这种体系架构的优越之处。     

数据仓库和数据挖掘技术在ERP中的应用

数据仓库和数据挖掘是近几年迅速发展起来的技术,主要用于构建企业的决策支持系统。文章根据数据仓库和数据挖掘技术的这个特点,并针对传统ERP系统在决策方面的不足,提出把数据仓库和数据挖掘应用到ERP中去,以数据仓库作为企业底层的数据源,再配合以各种数据挖掘技术,以提高ERP中的决策支持功能。     

基于数据仓库的数据采掘技术应用

本文首先分析了基于数据仓库的数据采掘的主要方法,技术和应用,然后通过实例讨论了数据采掘在股票分析与决策系统中的应用。