This paper discusses the problem of data mining in loosely coupled distributed information systems.
讨论了松散耦合的分布式信息系统中的数据挖掘问题。
Aiming at the problem of data mining in distributed system, a novel and effective meta-mining method is presented.
针对分布式系统中的数据挖掘问题，提出了一种新颖高效的分布式元挖掘方法。
Focusing on the problem of data mining in time-series, did research in transforming time-series to trend sequences and methods of performing data mining in acquired trend sequences.
本文针对时间序列的数据挖掘问题，研究了将时间序列转化为趋势序列，以及趋势序列中的数据挖掘问题。
It is a process of spatial knowledge discovery and data mining. It sums up a modeling problem of the GIS-based reservoir evaluation.
其本身就是一个空间知识发现和挖掘的过程，实质可归结为基于GIS的油气储层评价建模问题。
Discovering association rules between items in a large database is an important data mining problem as the number of association rule is usually very larger.
在大型数据库项目之间发现关联规则是一个重要的数据挖掘问题，而挖掘出的关联规则数目常常是巨大的。
Data mining is an important problem in KDD, and Rough set as a theory of set with fuzzy boundary is widely applied to infer classification rules from decision system.
数据挖掘是知识发现领域的一个重要问题，粗糙集理论是一种具有模糊边界的数据挖掘方法，它被广泛应用于决策系统的分类规则提取中。
Based on the summarization of data mining research, the problem of mining inter-transactional quantitative association rules are brought forward and defined in this paper.
在归纳现有关联规则研究的基础上提出了事务间数值型关联规则的数据挖掘问题，并对该问题进行了定义。
As new technology of data mining, support vector machines (SVM) have been successfully applied in pattern recognition and regression problem, et al.
支持向量机作为数据挖掘的一项新技术，应用于模式识别和处理回归问题等诸多领域。
Many real domain applications of MAS, such as information retrieval and data mining on the WWW can be reduced to the problem of searching in an unknown environment.
现在MAS中的许多具体应用问题也可以归结为对未知环境的搜索。比如WWW上的信息检索、数据发掘等。
The paper presents a framework of intelligent data mining based on the layer model. It consists of multi-layers of concepts, problem identifying lay er, task layer, adaptive layer and user layer.
在层次模型的基础上，提出了一个智能数据挖掘的开发框架，包括问题识别层、任务层、应用层和用户层等多层概念。
Discovering association rules between items in a large database is an important data mining problem. The number of association rules is usually very large.
在大型数据库项目之间发现关联规则是一个重要的数据挖掘问题，而挖掘出的关联规则数目常常是巨大的。
Support vector machine is a new technique of data mining, which is regarded as the best theory aimed at solving the problem of classification and regression of small sample pool at present.
支持向量机是数据挖掘的一项新技术，被认为是目前针对小样本的分类、回归等问题的最佳理论。
The way of generating frequent candidate a nd pruning technology are difficult technical problem when prenest traditional association rules mining algorithm is used to spatial data mining.
现有的传统关联规则挖掘算法构建频繁候选项的方式和修剪技术是其应用于空间数据挖掘的技术难题。
A new technology, data mining, is produced to solve the problem about abundance information but poverty of knowledge.
而数据挖掘这种新技术是为了解决当前“信息丰富而知识贫乏”的问题而出现的。
Absrtact: Disoovery of association rule is an important problem in database mining, but it is merely used to handle the discrete data.
摘 要：关联规则的发现是数据挖掘中的一个重要问题．但只是对离散型数据进行处理。
Abstract: Recently, the problem of Class-imbalance has become a hotspot in machine learning and data mining.
摘要：最近在机器学习和数据挖掘上，非平衡类问题成为了一个研究热点。
Decision tree is an important tool for data mining. However, the design of optimal decision tree is proved to be a NPcomplete problem.
决策树是一种重要的数据挖掘工具，但构造最优决策树是一个NP -完全问题。
Most quantitative association rules transform mining association rules of numeric property into boolean property, and the kernel problem is to divide the numeric data into intervals.
数值型关联规则的算法大多是将多值属性关联规则挖掘问题转化为布尔型关联规则挖掘问题，而连续属性的离散化是数值型关联规则的核心问题。
This paper addresses the problem that automatic videoclassification has lower classification precision. An automatic video classification approach of integrating data mining is proposed.
针对自动视频分类工作中分类预测精度低的问题，提出了一种集成数据挖掘技术的自动视频分类方法。
Absrtact: the problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.
摘要：高维数据之间的相似性度量问题是高维空间数据挖掘中所面临的问题之一。
Meanwhile, the paper discusses the problem of data visualization, and resolves it using neural network technique, describes the whole process of building a neural network data mining system.
本论文还简要讨论了在数据库中发现知识的数据可视化问题，并采用神经网络技术解决该问题，描述了建立一个神经网络数据挖掘的全过程。
Used of the data statistics, data mining, model analysis and other methods, mainly deal with the following two questions:1. Multi-attribute associations (select) problem.
研究中主要用到了数据统计法、数据挖掘法、模型分析法等多种方法，主要解决以下两个问题：1、多属性关联（查询）问题。
In this paper, labeled ordered tree is used as the data model of semi structured data, the problem of maximum tree structured frequent pattern mining from semi structured data is studied.
本文以标记有序树作为半结构化数据的数据模型，研究了半结构化数据的树状最大频繁模式挖掘问题。
Data mining is a kind of program to solve the problem of information explosion.
数据挖掘是信息爆炸问题的一种解决方案。
Frequent pattern mining is a fundamental data mining problem for which algorithms still suffer from inefficiencies because of the inherent complexities.
频繁模式挖掘是最基本的数据挖掘问题，由于内在复杂性，提高挖掘算法性能一直是个难题。
Consequently, how to find useful information from sets of data becomes a problem need to be solved imminently. Data mining technology comes into being in this background.
如何从中发现有用的信息，已经成为一个迫切需要解决的问题，数据挖掘技术在这种背景下应运而生。
Cluster analysis is an important research problem in the domain of data mining.
在数据挖掘领域中，聚类分析是一项重要的研究课题。
So how to use all kinds of successful technologies of data mining to find out valuable information from huge Email data becomes a problem that urgently to be resolved.
如何利用目前各种成熟的数据挖掘技术，从海量电子邮件信息中挖掘出有用的知识和信息，成为了亟待解决的热点问题。
So how to use all kinds of successful technologies of data mining to find out valuable information from huge Email data becomes a problem that urgently to be resolved.
如何利用目前各种成熟的数据挖掘技术，从海量电子邮件信息中挖掘出有用的知识和信息，成为了亟待解决的热点问题。
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