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采用限制与多维技术的数据采掘 总被引:1,自引:0,他引:1
针对当今数据采掘中效率不够高的问题,提出了采用限制与多维技术来进行数据采掘,讨论了哪些种类的限制能运用到采掘过程中,设计了一个数据采掘系统结构。 相似文献
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基于大型数据仓库的数据采掘:研究综述 总被引:162,自引:3,他引:162
本介绍了数据采掘技术的总体研究情况,包括数据采掘的定义,与其他学科的关系,采掘的主要过程,分类和主要技术手段,作为例子介绍了关联规则采掘的研究,同时介绍了一些原型系统和商业产品以及主要应用领域,指出了数据采掘研究的挑战性以及目前的局限性。 相似文献
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决策支持系统中的数据采掘技术研究 总被引:3,自引:1,他引:2
本文论述了数据采掘的基本概念,分析了决策支持系统中常用的数据分析方法,数据采掘在决策支持系统中的地位、作用及OLAP数据采掘机制,讨论了在决策支持系统中进行数据采掘的难点,并提出一种解决方法,取得了较好效果。 相似文献
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基于空间数据仓库的数据采掘 总被引:6,自引:0,他引:6
文章介绍了数据采掘技术的定义、数据采掘的过程和主要技术手段以及空间数据仓库的定义、基本结构框架、处理流程和技术支持,分析了基于空间数据仓库的数据采掘特点。 相似文献
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Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mining method which is capable of uncovering hidden patterns of sales and market. Hence, a prototype business intelligence system (BIS) named correlation coefficient sales data mining system (CCSDMS) has been developed and successfully trial implemented in a selected reference site. A series of experiments have been conducted to evaluate the performance of the proposed system. The results generated by the BIS are compared with a well known market available data mining system. The proposed quantitative correlation coefficient mining method is found to possess higher accuracy, better computational effectiveness and higher predictive power. With the new approach, associations for product relations and customer periodic demands are revealed and this can help to leverage organizational marketing capital to enhance quality and speed of promotions as well as awareness of product relations. 相似文献
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欧阳烽 《数字社区&智能家居》2013,(9):5595-5597
基于SAS的Web使用日志用户聚类分析,即通过SAS数据挖掘工具将由Web使用日志数据经过数据转换和数据预处理后形成的用户事务表数据运用不同的方法进行聚类分析,以达到根据不同类别用户的需求对数字资源进行合理的采购和管理,为用户提供个性化服务的目的。 相似文献
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数据挖掘工具的应用与标准化 总被引:4,自引:0,他引:4
介绍了数据挖掘概念,给出了目前数据挖掘工具的主要分类及存在的问题,探讨了数据挖掘语言的发展对数据挖掘工具标准化的推 动作用,并对数据挖掘工具未来的发展进行了展望。 相似文献
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Seoung Bum KimWeerawat Jitpitaklert Sun-Kyoung ParkSeung-June Hwang 《Expert systems with applications》2012,39(2):2073-2081
Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts. 相似文献
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A support-ordered trie for fast frequent itemset discovery 总被引:2,自引:0,他引:2
Woon Y.-K. Ng W.-K. Lim E.-P. 《Knowledge and Data Engineering, IEEE Transactions on》2004,16(7):875-879
The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the SOTrielT (support-ordered trie itemset) and two synergistic algorithms to accompany it for the fast discovery of frequent itemsets. Experiments involving a wide range of synthetic data sets reveal that its algorithms outperform FP-growth, a recent association rule mining algorithm with excellent performance, by up to two orders of magnitude and, thus, verifying its' efficiency and viability. 相似文献
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数据挖掘(Data Mining)是目前IT业界的热点,其身影随处可见。数据挖掘技术在许多行业中得到了很好的应用,尤其是在市场营销中获得了成功,初步体现了其优越性和发展潜力。该文主要分析了数据挖掘、数据仓库,联机分析处理(OLAP分析)等基本概念及它们之间的联系,并简要介绍了数据挖掘工具和数据挖掘应用领域。 相似文献
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High-speed, wide-area networks have made it both possible and desirable to interconnect geographically distributed applications that control distributed collections of scientific data, remote scientific instruments and high-performance computer systems. Historically, performance analysis has focused on monolithic applications executing on large, stand-alone, parallel systems. In such a domain, measurement, postmortem analysis and code optimization suffice to eliminate performance bottlenecks and optimize applications. Distributed visualization, data mining and analysis tools allow scientists to collaboratively analyze and understand complex phenomena. Likewise, real-time performance measurement and immersive performance display systems-i.e. systems providing large stereoscopic displays of complex data-enable collaborating groups to interact with executing software, tuning its behavior to meet research and performance goals. To satisfy these demands, the authors designed Virtue, a prototype system that integrates collaborative, immersive performance visualization with real-time performance measurement and adaptive control of applications on computational grids. These tools enable physically distributed users to explore and steer the behavior of complex software in real time and to analyze and optimize distributed application dynamics 相似文献
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LU Yuan-rong 《数字社区&智能家居》2008,(6)
当今社会,数据无处不在,数据挖掘技术作为一种新的信息处理技术,从海量的数据中找出有潜在价值的数据规律或数据模型。用人工的方式难以实现这个目标,Weka是一种可用于数据挖掘的工具,数据挖掘用户可使用Weka执行数据预处理,分类,回归,聚类,关联规则等任务。以Weka自带的数据集为例,详细介绍作为易于使用的数据挖掘工具Weka的使用。 相似文献