1.Han brought forward the conceptof OLAM, namely OLAF Mining, in 1997. He started with the unification of the databasic of OLAF and data mining, gave the orientation that the multidimensional datamodel become the common data basic of OLAP and data mining, and support theapplications of OLAP and data mining simultaneously.
HAN于1997年提出了OLAM的概念，即OLAP Mining，他从统一两者的数据基础入手，提出在多维数据模型的基础上同时支持OLAP应用和数据挖掘应用的思路。
2.Mining Model is expressed with forms by OLE DB for data mining , and a new SQL-like DDL leverages developers to build data mining applications using SQL Server 2000.
OLE DB for Data Mining以表的形式表达数据挖掘模型对象,而且也提供一种类似SQL的DDL使开发者能更好地建立数据挖掘应用。 本文给出了一个关于运用SQL Server2000构建数据挖掘应用的实例。
3.Data mining provide good technology supportment for decision support system, and data mining tools can directly mining in large database for discovering potential knowledge in database.
数据采掘工具将为决策支持系统开发提供良好的技术支持，并且数据采掘工具还可以对现有的大型数据库直接Mining，以发现数据库中潜在的知识。
4.This article introduced a visualizing data-mining model with the name DVM, and implemented the cleaning and extraction of heterogeneous data sources with the technology of data mining, object-oriented and visualization.
以上海证券交易所提供实验数据为基础,系统地介绍了一个数据挖掘可视化模型DVM（Data-Mining Visual Model）,并采用数据挖掘、面向对象及可视化技术实现对数据源的清洗和提取。
5.Access pattern mining module fulfills the FAP-Mining algorithm.
访问模式挖掘模块实现了本文第四章提出的FAP-Mining算法。
6.This paper introduces the features,categories,and process of data mining and discusses the application of data mining technology to sci-tech information dissemination.
因此,本文着重介绍了数据挖掘(Data Mining)技术的特性、分类、流程以及目前在科技传播中的应用。
7.Technique of Text Mining can quickly and effectively abstract the useful information from the many data. Text Classification is one of the most important and popular techniques in Text Mining.
文本挖掘(Text Mining)技术就可以快速、有效的从海量的数据中提取出对用户有用的信息，而文本分类是文本挖掘中最重要和应用最广的一项技术。
8.Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data, which is deemed to one of a foreland of data mining system and a promising cross-subject.
数据挖掘(data mining)就是从大量的、不完全的、有噪声的、模糊的、随机的数据中，提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的过程，被信息产业界认为是数据库系统最重要的前沿之一，是信息产业最有前途的交叉学科。
9.Data mining[1] is a process of nontrivial extraction of implicit, previous, unknown and potentially useful knowledge from a large amount, incomplete of noisy, fuzzy and random data.
数据挖掘(Data Mining,又译作数据采掘)[1]:是一个从大型数据库中提取人们感兴趣的知识的过程。 这些感兴趣的知识是潜在的、事先不被人们所认知的、隐含的、有用的信息。
10.Thirdly, the thesis introduces in detail DHP (Direct Hashing and Pruning) algorithm and FARM (Fast Association Rule Mining) which is an algorithm improved on the basis of Apriori and DHP.
接着，详细介绍了DHP(Direct Hashing and Pruning)算法及在Apriori算法和DHP算法基础上的快速挖掘算法FARM(Fast Association Rule Mining)；
