SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

Xiwei Xu; Changhai Zhang

doi:10.1117/12.2053128

24 December 2013 SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

Xiwei Xu, Changhai Zhang

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672I (2013) https://doi.org/10.1117/12.2053128
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Now some sequential patterns mining algorithms generate too many candidate sequences, and increase the processing cost of support counting. Therefore, we present an effective and scalable algorithm called SPMBR (Sequential Patterns Mining based on Bitmap Representation) to solve the problem of mining the sequential patterns for large databases. Our method differs from previous related works of mining sequential patterns. The main difference is that the database of sequential patterns is represented by bitmaps, and a simplified bitmap structure is presented firstly. In this paper, First the algorithm generate candidate sequences by SE(Sequence Extension) and IE(Item Extension), and then obtain all frequent sequences by comparing the original bitmap and the extended item bitmap .This method could simplify the problem of mining the sequential patterns and avoid the high processing cost of support counting. Both theories and experiments indicate that the performance of SPMBR is predominant for large transaction databases, the required memory size for storing temporal data is much less during mining process, and all sequential patterns can be mined with feasibility.

Citation Download Citation

Xiwei Xu and Changhai Zhang "SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672I (24 December 2013); https://doi.org/10.1117/12.2053128

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