期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2014
卷号：5
期号：4
页码：63-66
语种：English
出版社：Ayushmaan Technologies
摘要：Sentence clustering plays an important role in many text processing activities. Irrespective of the specific task (e.g., summarization, text mining, etc.), most documents will contain interrelated topics or themes, and many sentences will be related to some degree to a number of these. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an Spectral Clusteringclustering scheme to extract sparse social dimensions. For mining different associations of mining behavioral features like user activities and temporal spetial information collected from different social media, and integrates them with social networking information to improve prediction performance. To integrate these sources of information, it is necessary to identify individuals across social media sites. It consists of three key components: The first component identifies users’ unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification.
