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Enhancing the Labelling Technique of Suffix Tree Clustering Algorithm

机译：增强后缀树聚类算法的标注技术

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Clustering the results of a search helps the user to overview the information returned. In this paper, welook upon the clustering task as cataloguing the search results. By catalogue we mean a structured labellist that can help the user to realize the labels and search results. Labelling Cluster is crucial becausemeaningless or confusing labels may mislead users to check wrong clusters for the query and lose extratime. Additionally, labels should reflect the contents of documents within the cluster accurately. To be ableto label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. Wealso present a new metric that employs to assess the success of cluster labelling. We adopt a comparativeevaluation strategy to derive the relative performance of the proposed method with respect to the twoprominent search result clustering methods: Suffix Tree Clustering and Lingo.
机译：对搜索结果进行聚类有助于用户概述返回的信息。在本文中，我们将聚类任务视为对搜索结果进行分类。目录是指结构化的标签列表，可以帮助用户实现标签和搜索结果。标记群集至关重要，因为毫无意义或令人困惑的标签可能会误导用户为查询检查错误的群集并浪费加班时间。此外，标签应准确反映群集中文档的内容。为了能够有效地标记群集，引入了一种新的群集标记方法。除了发现文档簇以外，更着重于/产生易于理解和准确的簇标签。我们还提出了一种新的指标，用于评估集群标记的成功。对于两种突出的搜索结果聚类方法：后缀树聚类和Lingo，我们采用比较评估策略来推导该方法的相对性能。

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