文章基本信息

标题：Securely Mining User Sequential Pattern Matching in Document Streams
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作者：K. Radhika ; S J Sowjanya ; Yerragudipadu Subbarayudu 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：2
页码：2109
DOI：10.15680/IJIRCCE.2017.0502192
出版社：S&S Publications
摘要：The process of data mining produces various patterns from a given data source. The most recognizeddata mining tasks are the process of discovering frequent item sets, frequent sequential patterns, frequent sequentialrules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Inmy research work Textual documents created and distributed on the Internet are ever changing in various forms inorder to characterize and detect personalized and abnormal behaviors of Internet users, proposeing Sequential TopicPatterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) indocument streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can beapplied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. Research work considerthree phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all theSTP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by makinguseraware rarity analysis on derived STPs. Experiments shows that our approach can indeed discover special users andinterpretable URSTPs effectively.
关键词：KDD; User-aware Rare STPs Cross Layer; Failure Recovery; Link Stability