Path Tree: Mining Sequential Patterns Efficiently in Data Streams Environments

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)


Although issues of data streams have been widely studied and utilized, it is nevertheless challenging to deal with sequential mining of data streams. In this paper, we assume that the transaction of a user is partially coming and that there is no auxiliary for buffering and integrating. We adopt the Path Tree for mining frequent sequential patterns over data streams and integrate the user’s sequences efficiently. Algorithms with regards to accuracy (PAlgorithm) and space (PSAlgorithm) are proposed to meet the different aspects of users. Many pruning properties are used to further reduce the space usage and improve the accuracy of our algorithms. We also prove that PAlgorithm mine frequent sequential patterns with the approximate support of error guarantee. Through experiments, synthetic dataset is utilized to verify the feasibility of our algorithms.


Data Mining Sequential Patterns Frequent Patterns 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Dong Hwa UniversityHualienTaiwan, R.O.C

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