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.
针对聚类算法的中心点问题，提出了相应的层次编码型数据的快速处理算法，并从理论上证明了算法的正确性。
The paper also proposes a fast algorithm to compute the median of a hierarchy coding data set, and gives a clear proof of the algorithm.
除了文学类文章外，其他文章都有清晰的结构，每个段落都有中心点，在扫读的时候可以自己尝试总结。 。
That tbeing said, each passage(except Literature) has a clear argumentative structure. One paragraph alwayshas a focus. Condition yourself to summarize paragraphs as you skim them.
CLARANS算法是经典的划分聚类算法，其核心思想是采用随机重启的局部搜索方式搜索中心点。
As a classical partition clustering algorithm, CLARANS USES local search with random restart to find clusters central points.
除了文学类文章外，其他文章都有清晰的结构，每个段落都有中心点，在扫读的时候可以自己尝试总结。 。
That being said, each passage(except Literature) has a clear argumentative structure. One paragraph alwayshas a focus. Condition yourself to summarize paragraphs as you skim them.
之后再通过竞争网络对获取到的中心点进行训练，使中心点更加靠近每一类的中心。
And then, by training the obtained central point via competition network, the central point can be made closer to the center of each cluster.
之后再通过竞争网络对获取到的中心点进行训练，使中心点更加靠近每一类的中心。
And then, by training the obtained central point via competition network, the central point can be made closer to the center of each cluster.
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