实验结果表明该方法的有效性，将该方法用于搜索结果聚类这一应用中。
Experimental results show that the method is effective, and the method is used to describe the search result clustering.
最后，使用增量式的概念格生成算法对搜索结果片段进行概念聚类，并从中产生每个聚类的主题。
Finally, incremental algorithm of producing concept lattice is used to carry on concept clustering to the passage of search results, and produced the theme of each cluster result from it.
针对目前搜索引擎搜索结果中普遍存在大量重复网页的现象，提出了一种基于聚类算法DBSCAN的搜索结果优化算法。
The search results got by current search engines generally include a large number of duplicate Web pages. A search results optimization algorithm based on DBSCAN clustering algorithm was proposed.
聚类搜索的目的就是为了快速帮助用户寻找信息，它的突出特点是根据某一属性，对搜索返回的结果进行聚类。
Clustering search's purpose is to help users find information quickly, it's outstanding feature is based on a property, on the search results returned by the cluster.
结果表明，系统聚类算法在这四种算法中最有效，而峰值搜索法优于模糊C均值和诱导的模糊c分划算法。
The results obtained indicate that HCAM is the most powerful algorithm of the four, and that GFAM is more powerful than FCM and IFC...
该算法选取源搜索结果中排名靠前的部分网页，对这部分网页根据网页相似度进行DBSCAN聚类，最大限度剔除冗余网页，实现搜索结果的优化。
The algorithm selected top-ranking Web pages of source search results, and clustered them to remove as much redundant pages as possible according to page similarity to achieve optimal search results.
该算法选取源搜索结果中排名靠前的部分网页，对这部分网页根据网页相似度进行DBSCAN聚类，最大限度剔除冗余网页，实现搜索结果的优化。
The algorithm selected top-ranking Web pages of source search results, and clustered them to remove as much redundant pages as possible according to page similarity to achieve optimal search results.
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