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首页> 外文期刊>IAENG Internaitonal journal of computer science >Extracting Attractive Local-Area Topics in Georeferenced Documents using a New Density-based Spatial Clustering Algorithm
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Extracting Attractive Local-Area Topics in Georeferenced Documents using a New Density-based Spatial Clustering Algorithm

机译：使用新的基于密度的空间聚类算法提取地理参考文档中有吸引力的局部主题

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摘要

Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media sites via the Internet, allowing people to transmit and collect geographic information. Typically, such georeferenced documents are related not only to personal topics but also to local topics and events. Therefore, extracting "attractive" areas associated with local topics from georeferenced documents is currently one of the most important challenges in different application domains. In this paper, a novel spatial clustering algorithm for extracting "attractive" local-area topics in georeferenced documents, known as the ((€), σ)-density-based spatial clustering algorithm, is proposed. We defined a new type of spatial cluster called an ((€), σ)-density-based spatial cluster. The proposed density-based spatial clustering algorithm can recognize both semantically and spatially separated spatial clusters. Therefore, the proposed algorithm can extract "attractive" local-area topics as ((€), σ)-density-based spatial clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site were used. The experimental results showed that the ((€), σ)-density-based spatial clustering algorithm could extract "attractive" areas as the ((€), σ)-density-based spatial clusters that were closely related to local topics.
机译：随着社交媒体的普及，大量地理参考文档（包括位置信息）通过Internet发布在社交媒体网站上，使人们能够传输和收集地理信息。通常，此类地理参考文档不仅与个人主题相关，而且与本地主题和事件相关。因此，从地理参考文档中提取与本地主题相关的“有吸引力”区域目前是不同应用程序领域中最重要的挑战之一。在本文中，提出了一种新颖的空间聚类算法，用于提取地理参考文档中的“有吸引力”的局部主题，即基于（（€），σ）密度的空间聚类算法。我们定义了一种新型的空间簇，称为基于密度的（（€），σ）。提出的基于密度的空间聚类算法可以识别语义上和空间上分离的空间聚类。因此，提出的算法可以提取“有吸引力的”局部区域主题，作为基于（（€），σ）密度的空间聚类。为了评估我们提出的聚类算法，使用了在Twitter网站上发布的带有地理标签的推文。实验结果表明，基于（（€），σ）密度的空间聚类算法可以提取“吸引人”区域，作为与（本地）主题密切相关的基于（（€），σ）密度的空间聚类。

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