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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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