Rough Set is a tool to deal with vague and uncertain data, therefore it becomes an important frame in DM.
粗集是一种处理模糊和不确定性数据的工具，因而成为数据挖掘中的重要框架。
DM can find useful information and knowledge effectively from much customer 's data, and then promote effectively quality of CRM, it reaches the aim which can raise the bank competition.
数据挖掘技术可以有效地从大量的客户数据中发现有用的信息和知识，进而可以有效提升客户关系管理的质量，达到提高银行竞争力的目的。
Methods The data from clinical manifestations and coronary angiography were comparatively analyzed between 25 patients of CD with type -2 DM and 95 patients without type-2 DM.
方法对比分析25例冠心病合并2型糖尿病患者与同期95例冠心病不合并2型糖尿病患者的临床和冠状动脉造影资料。
This paper introduces DM (data mining) and its work procedure, and then points out some problems which should be considered in data mining, finally gives a concrete sample of data mining.
简要介绍了DM(数据挖掘)及其工作过程，并指出了数据挖掘过程中应注意的问题，最后给出了一个具体的数据挖掘的例子。
The DM and OLAP are the critical techniques of data analysis, the multi dimensional data mining model combining both techniques can enhance the performance and effects of data analysis.
DM技术与OLAP技术是电力营销决策支持系统中的关键数据分析技术，二者有机结合构成的多维数据挖掘模型能提高数据分析的效果和性能。
OBJECTIVE To set up the prediction model for evaluating the risk of diabetes mellitus(DM)in community by application of decision tree with data from health records.
目的利用居民健康档案数据和决策树方法，建立糖尿病预测模型，建立社区新型糖尿病高危人群筛选模式。
Data mining (DM) is to extract knowledge from huge datasets, the purpose of which is to find the useful patterns hidden behind the data.
数据挖掘(DM)就是从大型数据集中抽取知识，其目的是发现深藏在一般数据之中的有用模式。
Traditional DM (data Mining), basically, is a data-analyzing tool for local data at present and only can produce few of generalized or understood knowledge from local datasets.
传统的数据挖掘基本上是一个本地的数据分析工具，仅能对本地数据集产生一定的理解性或概括性的知识。
OLAP and DM are analytical tools aiming at business data in the business intelligence system.
在商务智能系统中，联机分析处理和数据挖掘是针对商业数据的分析工具。
Several issues related with Tax decision making are studied and applied, including data warehouse(DW), online analyses and processing (OLAP) and data mining (DM).
笔者对涉及到税务支持的几个关键问题进行了理论探讨和实际应用，包括数据仓库（DW）的建立和组织、联机分析（OLAP）和数据挖掘（DM）。
Data Mining (DM) is the knowledge discovery from databases.
数据挖掘(DM)是从数据库中发现知识。
The author takes CRISP-DM as the referenced model of the data mining process.
作者以CRISP-DM作为数据挖掘过程的参考模型。
Our data demonstrate that long-term surial is acceptable in heart transplant recipients with preoperatiely diagnosed DM and DRCs.
我们的研究数据表明，对被诊断为伴有并发症的糖尿病患者进行心脏移植，他们的长期存活率是可以接受的。
The method uses DM technology to select, to analyze and to predict data, and uses data visualization technology to show data chart.
该方法是以数据挖掘技术解决数据的选取、分析和预测，以数据可视化技术实现数据的表现。
The clinical data with93cases with EC/CC complicated with DM were analyzed.
分析93例食管癌和贲门癌合并糖尿病的临床资料。
A concept of broad sense data space was proposed so that DM can be made in different part and different abstract hierarchy in database and data warehouse, which enhances the functions and flexibility.
提出广义数据空间的概念，使得数据挖掘能够在数据库或数据仓库的不同部位或不同的抽象级别上，对数字数据或者文本数据进行挖掘，加强了决策分析的功能和灵活性。
Methods: The clinical data of 156 patients with DM including 44 patients who had died were analyzed retrospectively.
方法：对156例DM患者的临床资料进行回顾性分析，其中病死44例。
So in facing of challenge of "Much data, but little information", there come Data Mining (DM) and Knowledge Discovery in Database (KDD).
因此，面对“数据丰富，但信息贫乏”的挑战，数据挖掘和知识发现技术应运而生。
Data Warehouse supply the data sources to the OLAP and DM.
数据仓库直接为联机分析处理和数据挖掘提供数据源。
The paper's main tasks are as follows:(1) Some research was developed which was about the theory and technology of DSS, data warehouse (DW), Online Analytical Processing ( OLAP ) , data mining(DM).
论文的主要工作如下：（1）对决策支持系统、数据仓库、联机处理分析OLAP、数据挖掘相关理论和技术进行了研究。
Tranditional DM (Data Mining), basically, is a data-analyzing tool for local data at present and only can produce few of generalized or understood knowledge from local datasets.
传统的数据挖掘基本上是一个本地的数据分析工具，仅能对本地数据集产生一定的理解性或概括性的知识。
Firstly, the basic concepts of data mining (DM) and data warehouse are presented, and their characteristics and current significance are introduced.
从数据挖掘和数据仓库的基本概念入手，简要介绍了数据挖掘和数据仓库的特点和其研究的现实意义。
Input data appearing on the data bus, is written to the memory array subject to the DM input logic level appearing coincident with the data.
数据总线上的输入数据是否写入存储器，取决于此时的DM的输入逻辑。
DM deepens the data analysis, it automatically mines the interesting model hiding in mass data.
数据挖掘是数据分析的深化，它可自动的挖掘出隐藏在大量数据中有趣的模式。
Take CRISP-DM as guide principle, we divide data mining into 6 steps from business perspective. At last, this text desighned a data mining system for civil engineering estimation.
以“跨行业数据挖掘过程标准”（CRISP-DM）为指导准则，从商业的角度将土建工程概算的数据挖掘过程分6个阶段有效的执行。
Data mining (DM) is a new hot research point in database area. Data mining gets knowledge from large quantity of data.
数据挖掘是近年来数据库领域中出现的一个新兴研究热点，它是从大量数据中获取知识。
Methods 91 patients with DM accepted health education on DM and CGMS, then accepted CGMS examination. The side effect and the integrity of blood glucose data were analyzed.
方法对91例DM患者进行教育和护理指导，然后进行CGMS检查，观察血糖监测期间的不良反应和监测结果的完整性。
Methods 91 patients with DM accepted health education on DM and CGMS, then accepted CGMS examination. The side effect and the integrity of blood glucose data were analyzed.
方法对91例DM患者进行教育和护理指导，然后进行CGMS检查，观察血糖监测期间的不良反应和监测结果的完整性。
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