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.

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。

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Experimental results show this algorithm is equal to DBSCAN, and can solve the increment clustering problem when the batch data is updated effectively.

实验结果表明，该算法与DBSCAN是等价的，能更有效地解决批量数据更新时的增量聚类问题。

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Proposes an improved DBSCAN algorithm which can handle non-spatial properties and greatly accelerate the speed of clustering.

文中提出了一种基于DBSCAN的算法，可以处理非空间属性，同时又可以加快聚类的速度。

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