基于查询诱导子图随机划分的搜索结果聚类
Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs
摘要：
在本文中，我们提出了一种方法来搜索结果聚类，使用分区的基础链接图。我们定义了“查询诱导子图”的概念，并将搜索结果聚类问题转化为将给定子图有效划分为主题相关聚类的问题。此外，我们提出了一种新的算法，这种图的近似分区，这导致在集群质量相媲美的确定性算法获得的，同时在更有效的计算时间，适合于实际应用。最后，我们提出了一个实用的聚类搜索引擎开发作为本研究的一部分，并使用它来获得有关所提出的概念的现实世界中的性能的结果。
英文摘要：
In this paper, we present an approach to search result clustering, using partitioning of underlying link graph. We define the notion of "query-induced subgraph" and formulate the problem of search result clustering as a problem of efficient partitioning of given subgraph into topic-related clusters. Also, we propose a novel algorithm for approximative partitioning of such graph, which results in cluster quality comparable to the one obtained by deterministic algorithms, while operating in more efficient computation time, suitable for practical implementations. Finally, we present a practical clustering search engine developed as a part of this research and use it to get results about real-world performance of proposed concepts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
