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Online Clustering for Topic Detection in Social Data Streams

机译：在社交数据流中有主题检测的在线聚类

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Microblogs have become an important origin of information regarding events happening in a location during a time period. Analyzing and clustering these streams of short textual messages is an important research activity which is attracting the interest of both public and private organizations, since the extracted knowledge can be exploited to enhance the comprehension of people behavior and the onset of emergency situations. Clustering these streams requires efficient algorithms capable of analyzing this continuos deluge of data. The paper proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroids generated so far. The assignment of a tweet to a centroid uses a similarity measure that takes into account both the cluster age and the terms occurring in the tweet. Experiments on messages posted by users in the Manhattan area show that the method is able to extract events effectively taking place in the examined period.
机译：微博已成为在时间段内发生的事件的重要信息的重要性。分析和聚类这些短文本信息流是一种重要的研究活动，这是吸引公共和私人组织的兴趣，因为可以利用提取的知识来增强人们行为的理解和紧急情况的发作。群集这些流需要能够分析此连续数据的高效算法。本文提出了一种在线算法，逐步将缩短流逐渐分成群集。该方法总结了到目前为止生成的群集质心的检查推文。 Tweet将推文分配给CentroID使用相似度措施，以考虑群集年龄和推文中发生的术语。曼哈顿用户发布的信息实验表明，该方法能够在检查期间有效地提取事件。

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