To address this problem, a collaborative filtering based on user clustering strategies to improve the basic idea is the basis of user-based clustering of users and more interested in that.
基于此不足，在用户聚类协同过滤算法的基础上进行了改进，其基本思想是在基于用户聚类的基础上研究用户多兴趣的表示。
Clustering allows a user to make groups of data to determine patterns from the data.
群集让用户可以通过数据组来从数据确定模式。
However, for the average user, clustering can be the most useful data mining method you can use.
不过，对于一般的用户，群集有可能是最为有用的一种数据挖掘方法。
A major disadvantage of using clustering is that the user is required to know ahead of time how many groups he wants to create.
使用群集的一个主要劣势是用户需要提前知道他想要创建的组的数量。
I'll demonstrate clustering using vectors produced from Wikipedia documents, but the vectors can come from other areas, such as sensor data or user profiles.
我将使用通过Wikipedia文档生成的矢量来演示集群，但是也可以从其他地方获取矢量，比如说传感器数据或用户资料。
Clustering technologies often make use of a fast communication protocol, such as User Datagram protocol (UDP) or Multicast protocols.
集群技术通常利用快速通信协议，比如用户数据报协议(user Datagram Protocol,UDP)或多播协议。
Firstly, tag 's meaning for user profile is proved, and clustering algorithms based on social tagging from the aspects of users, resources and tags are discussed.
首先是明确了标签对于用户模型的意义，接着，从用户、资源和标签三个角度对基于社会化标注的聚类算法进行了讨论。
Clustering analysis is based on calculating the similarity degree of the user access paths.
对客户进行聚类分析是建立在对用户浏览路径进行相似程度计算的基础上的。
This paper use weighted directed graph to describe user visit and conversation records, and use clustering algorithms to realize the page clustering by the weighted directed graph mode established.
利用有向带权图表示用户的访问会话记录，对建立的有向带权图模型运用聚类算法实现页面聚类。
It will output a low quality clustering result if user set unsuitable parameters before clustering operation.
而设置不合理的聚类参数又使得聚类结果质量变低。
And select different initial core nodes of the data also will affect the effectiveness of clustering algorithm, so the user generally will not get an accurate clustering.
并且初始聚类中心的选取不同也同样会影响聚类算法的效果，因此用户一般不会得到准确的聚类。
After analyzing the disadvantages of the user profile based on-keywords vector in the existing document recommendation system, a novel representation of user profile based on clustering was proposed.
分析了现有文章推荐系统中基于关键词向量的用户模型表示方法存在的不足，提出了基于聚类兴趣点的用户模型表示方法。
After analyzing the current automatic user modeling approaches, automatic modeling based on user interests clustering is proposed.
分析了现有的自动用户建模方法，提出基于用户兴趣聚类进行自动用户建模。
After analyzing the current automatic user modeling approaches, automatic modeling based on user interests clustering is proposed.
分析了现有的自动用户建模方法，提出基于用户兴趣聚类进行自动用户建模。
应用推荐