基于短信文本信息流的多热点事件挖掘

短信文本信息流携带了丰富的信息资源,为了在其中挖掘出多热点事件,给出了短信文本信息流在线分检算法,该方法采用特征词共现频度定义了特征词相关度,综合前导信息集合及信息产生频率定义了短信文本相似度。并且每聚类到一个时间段后,就对已聚类的短信文本进行周期分类。该算法对大数量短文本信息流的多热点事件检索效率较高,同时减少了信息的误检和漏检的可能性。在真实数据集上与Single-Pass算法进行比较实验,其结果表明了各项指标都有不同程度的提高。

The mining of hot events based on information flow of short message texts

The text information flow of SMS had carried abundant information resources. In order to find out the hot events behind it, an online sorting algorithm was given for the text information flow of SMS. This method used the co-occurrence frequency of feature words to define its relevance. And the similarity of message texts was defined on the basis of preamble information collection and information frequency. Furthermore, after each time period of clustering, the clustered SMS texts were classified periodically. This algorithm had higher efficiency to find hot events for a large number of short text information retrieval. Moreover, this algorithin reduced the possibility of false detection and missed detection. Based on the comparison of experiments on algorithms between real data sets and Single-Pass, the results showed that each index was improved to some degree.