求翻译：要想获得好的聚类效果，首先要解决的就是距离度量问题，传统的层次聚类算法大多仅仅依赖欧式距离，没有考虑数据点周围对象的分布情况，以及数据结构不同对聚类结果的影响，很显然聚类准确度往往不好。是什么意思？

To get a good clustering effect , we must first solve the problem is the distance metric , traditional hierarchical clustering algorithms are dependent on Euclidean distance only , without considering the distribution of data points around the object , as well as different data structure on the clus

If you want to obtain a good clustering results, it is first necessary to resolve is the measure of the distance, traditional hierarchy Clustering algorithm are mostly just rely on Euro-style distance, they do not take into account data points around the object, as well as different data structure f

Must want to obtain the good cluster effect, first must solve is away from the measure question, the traditional level cluster algorithm relies on the western-style distance mostly merely, had not considered around the data point the object distributed situation, as well as the construction of data

To achieve good clustering effect, as a distance measure problems must be resolved first, most of the traditional hierarchical clustering algorithm depend only on Euclidean distance, did not take into account the distribution of data points around objects, effects of different clustering results and

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