顺序聚类算法是一种非常直接和快速的算法，并且不需要提前确定聚类个数。
Sequential algorithm is a straightforward cluster algorithm, and people do not have to provide the number of clusters in advance.
均值算法的聚类个数k需指定，聚类结果与数据输入顺序相关，而且易受孤立点影响。
K-means algorithm has some deficiencies. The number K must be pointed and its effectiveness liable to be effected by isolated data and the input sequence of data.
先聚类后枚举所有划分的方法，聚类个数的确定会很大程度影响计算结果和运行效率。
The result and efficiency of method, in which first clustering and then enumerating all the possible cases, were greatly affected by the number of clusters.
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.
通过对系统的性能测试，新的自适应聚类索引算法，聚类的效果不再受到聚类个数和聚类中心点的限制。
Based on the performance tests, clustering results will no longer be restricted by the number of clusters and initial center.
聚类技术通常必须指定一个聚类个数，这样给出的聚类结果是否合理，是否真正反映了用户群的分类就需要进行聚类有效性的验证。
Clustering techniques usually have to assign the number of clusters, but whether the result really reflects the classification of users needs verification on the validity of cluster.
聚类是一种把整个数据库分成不同的群组，使群与群之间差别很明显，而同一个群之间的数据尽量相似的算法。
Cluster is an algorithm, which can divide the data in the database into different groups, and there are obvious distinctions among groups.
实验表明，EPFCM算法可以有效地得到最佳的类中心个数，聚类结果不受初始类中心影响，并且陷入局部极小的概率较FCM算法大大降低。
Experiments show that EPFCM algorithm can gain best cluster centers and optimal cluster structures, and the probability of falling into local minima is greatly reduced.
最后，研究并讨论了聚类正确率和集成规模、簇的个数之间的关系。
Finally, we also study and discussion the relationship between accuracy and ensemble size, the number of clusters, respectively.
实验证明新算法有效解决了调和K均值算法中簇个数需事先给定及聚类算法容易陷入局部最优的问题。
The result of experiment indicate that the new algorithm efficiently resolves the problems of KHM algorithm that the count of clusters need decide prior and it well reach local optimum result.
目的探讨六种常见的条件系统聚类法的性质，并选择一到两个适于二维有序样品聚类的样品个数比较均匀的条件系统聚类法。
Objective Six familiar conditional hierarchical clustering methods were discussed, and some methods of 2-dimensional ordinal sample were selected which results were relative even.
目的探讨六种常见的条件系统聚类法的性质，并选择一到两个适于二维有序样品聚类的样品个数比较均匀的条件系统聚类法。
Objective Six familiar conditional hierarchical clustering methods were discussed, and some methods of 2-dimensional ordinal sample were selected which results were relative even.
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