本文将聚类思想引入到关联规则挖掘中。
In this paper, we combine cluster method with mining association rules.
本系统给出了基于关联规则挖掘和基于用户事务模式聚类两种推荐算法。
The system gives two kinds of recommendation algorithms based on association rule mining and user's transaction pattern clustering.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
针对一类常见而简单的规则中有项或缺项的约束，提出了一种基于事务数据修剪的约束关联规则的快速挖掘算法。
Aiming at a familiar and simple constraint that some items must or must not present in rules, a fast clipped-transaction-based constraint association-rule mining algorithm was put forward.
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.
在分布式关联规则挖掘中，首先需要解决分布式环境下的聚类分区问题。
The key problem in distributed association rules mining is to cluster partition in distributed environment.
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.
规则聚类将关联规则挖掘产生的大量规则重新组织，帮助用户发现感兴趣的规则。
Rule clustering re-organizes rules which are generated during the procedure of association rule mining, to help customers find out interested rules.
空间关联规则挖掘是在空间数据库中进行知识发现的一类重要问题。
Spatial association rule discovery in spatial databases is a very important data mining task.
同时本篇论文也主要提出了一些经常被使用的数据挖掘的算法像聚类挖掘、关联规则挖掘、序列模式挖掘等。
Also, some of data mining algorithms that are commonly used in Web Usage mining are clustering, association rule generation, sequential pattern generation etc.
它包含关联规则挖掘、预测、分类、聚类、演化分析等多种技术手段。
It includes lots of measures such as association rules mining, classification and prediction, clustering analysis and evolvement analysis.
基于顾客偏好随时间变化的特性，采用聚类、关联规则等技术，对顾客偏好进行动态挖掘。
According to the characteristics of customer preference that changes with time, customer preferences are mined dynamically with such technologies as clustering and association rules.
用关联规则的数据挖掘方法探讨了古今医家用四物汤类方治疗痛经的用药规则，通过比较其置信度，确定古今医家在用药规律上无显著差别。
With data mining method, the authors made a study on the regularities of drug use in the four-drug decoction and the like for dysmenorrhea.
摘 要：提出了一种基于关联规则挖掘的聚类方法。
Absrtact: Presents a method of clustering based on mining association rules.
其中关联规则是通过分析数据库中频繁出现的数据之间的联系得到的一类规则，是数据挖掘技术应用的最经典案例。
Association rules are a kind of rules obtained by analyzing the links between the frequent data in the database, which is the most classic example of data mining technology.
数据挖掘是本课题的研究核心，主要包括关联规则发现、数据聚类和数据分类。
Data mining is the core topic of this paper. Basically, it includes associate rule founding, data clustering and data assorting.
在分布式关联规则挖掘中首先需要解决分布式环境下的聚类分区问题，该文基于CURE的工作原理，提出了D -CURE算法。
The key problem in distributed association rules mining is to cluster partition in distributed environment. This paper presents an algorithm called D-CURE which is based on the principle of CURE.
实验表明性能优于相等权重的聚类方法和不进行聚类直接从所有顾客中进行关联规则挖掘的方法。
The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a non-clustering method.
实验表明性能优于相等权重的聚类方法和不进行聚类直接从所有顾客中进行关联规则挖掘的方法。
The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a non-clustering method.
应用推荐