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.
数据聚类在数据挖掘、模式识别、图像处理和数据压缩等领域有着广泛的应用。
Clustering is a promising application technique for many fields including data mining, pattern recognition, image processing, compression and other business applications.
聚类可以对数据进行有效分析，在数据挖掘、数值分析、模式识别等领域有着非常广泛的应用。
Clustering can analyse the data effectively, which has a wide use in many fields, such as data mining, numerical analysis and pattern recognition.
在数据挖掘领域，聚类用于发现数据的分布模式和数据间的相互关系。
In data mining, clustering is used to discover groups and identify interesting distribution in the underlying data.
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.
数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
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.
基于层次聚类框架，设计了一种有效的挖掘压缩序列模式的算法CSP。
Based on the hierarchical clustering framework, an effective algorithm CSP is developed to mine compressed sequential patterns.
基于层次聚类框架，设计了一种有效的挖掘压缩序列模式的算法CSP。
Based on the hierarchical clustering framework, an effective algorithm CSP is developed to mine compressed sequential patterns.
应用推荐