粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
研究模糊聚类分析在医学图像数据挖掘中的应用。
To study the application of fuzzy cluster analysis for medical image data mining.
讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。
Mining algorithm and prediction method of fuzzy association rules are discussed in this paper.
为解决单个帖子线索的多话题性问题，识别聚类中的孤立点，提出一种基于模糊聚类的网络论坛（BBS）热点话题挖掘算法。
A bulletin board system(BBS) hot topic mining algorithm based on fuzzy clustering was developed to solve the problem of the post thread with multiple topics and identifying the outliers in clustering.
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。
Rough Set is a tool to deal with vague and uncertain data, therefore it becomes an important frame in DM.
目前微粒群算法已广泛应用于函数优化、神经网络训练、数据挖掘、模糊系统控制以及其他的应用领域。
Recently, Particle Swarm optimization is applied into function optimization, Neural Networks, data mining, Fuzzy Control System and other application field.
设计了一种基于粗糙集——模糊神经网络技术的数据挖掘与数据融合集成系统。
One kind of integrated data mining and data fusion system's model is designed with fuzzy neural network based on rough sets.
粗糙集理论是一种新的处理模糊和不确定性知识的数学工具，在人工智能及数据挖掘等众多领域已经得到了广泛的应用。
Rough set theory is emerging as a powerful tool for dealing with vagueness and uncertainty of facts, which has important applications to artificial intelligence and data mining.
在分析铁路客票数据特征的基础上，提出采用分段模糊BP神经网络对铁路客运量进行数据挖掘预测。
The segment fuzzy BP Neural Network is adopted to predict the passenger traffic volume of railways in data mining based on analyzing the data feature of railway passenger tickets.
本文把模糊集理论和传统的关联挖掘结合在一起，提出了一种模糊关联数据挖掘算法。
In this paper, we connected fuzzy set theory with association mining, and proposed a fuzzy association data mining arithmetic.
数据库中关联规则挖掘一直是人们研究的热点，而模糊集理论的应用又为这一领域注入了新的活力。
Mining association rules in databases is the hot point in people's researches and the application of fuzzy-set theory has added new energy into the field.
模糊粗糙集理论是一种处理不确定性信息的重要的数据挖掘方法。
Fuzzy rough set theory is an important method for data mining of uncertain data.
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。
Moreover, fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of TSDM is given then.
由于关系数据的竞争聚集算法能得到优化的固定的聚类个数，因此能挖掘出优化的模糊关联规则。
The optimal fuzzy association rules can be mined due to the optimal fixed clustering number that can be obtained by the relational competitive agglomeration algorithm.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
该方法基于半模糊核聚类算法挖掘不同类别之间的衔接和离散信息，设计树型支持向量机的树型结构，克服其差错积累问题。
The method mines information on overlap between classes, designs the tree structure and overcomes the misclassification of tree-structured SVMs based on the semi-fuzzy kernel clustering algorithm.
对数量型属性，应用竞争聚集算法将数量型属性划分成若干个模糊集，并系统地提出加权模糊关联规则的挖掘算法。
As for quantitative attributes, they are divided into several fuzzy sets by the competitive agglomeration algorithm, and then the algorithm for mining weighted fuzzy association rules is provided.
因此，研究如何有效地挖掘模糊序列模式变得尤为重要。
Therefore it gets very important to study how we can mine the fuzzy sequential patterns efficiently.
神经网络和模糊逻辑都是有效的数据挖掘方法。
Both neural network and fuzzy logic are valid method for data mining.
针对数据挖掘问题，将直觉模糊集与神经网络理论相结合，提出一种新的方法。
A new method was proposed for data mining problem which integrated intuitionistic fuzzy set and neural network theory.
针对流程工业数据的高维数、不确定的特点，研究适合处理流程数据的模糊集、粗糙集的粒度数据挖掘理论和方法。
According to the high dimensions and uncertainty of process industrial data, the fuzzy set and rough set of granularity data mining are studied for process data.
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。
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.
粗糙集理论以其出色的处理模糊和不确定知识的能力，成为数据挖掘研究中的有力工具。
Rough set, with its ability in dealing with uncertainty and vagueness, is widely applied in the study of data mining.
建立合适的隶属度函数是入侵检测中应用模糊数据挖掘所面临的一个难点。
Defining appropriate membership functions is a difficult task in fuzzy data mining to detect intrusions.
分别使用模糊聚类方法、混合模糊神经网络、关联规则挖掘等知识发现方法对间歇过程中的配方、周期性污垢、操作策略规则等进行挖掘和处理。
Using the Fuzzy Cluster method, Hybrid Fuzzy Neural Network, Association rules mining methods, etc. find and excavate the recipes, periodic fouling, and operation strategy rule in the batch process.
该文针对网管告警数据库中时间序列存在的连续性、不确定性和模糊性问题，提出了一种基于时态关联规则挖掘告警库的新方法。
For the problems of continuity, uncertainty and fuzziness in the time-series of the network management alarm database, this pa-per puts forward a new mining method based on time-series rules.
基于上述问题，提出了一个基于模糊数值约束的关联规则挖掘方法，实际挖掘结果表明这种方法是有效的。
Based on the above problem this paper presented a method called association rules mining with fuzzy quantitative constraints.
该文基于数据挖掘技术，提出了一种模糊数据挖掘方法。
The paper proposes a method of fuzzy data mining (FDM) based on data mining technology.
该文基于数据挖掘技术，提出了一种模糊数据挖掘方法。
The paper proposes a method of fuzzy data mining (FDM) based on data mining technology.
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