Abstract—Sequential pattern mining is applicable in a wide range of applications since many types of data sets are in a time related format. Besides mining sequential patterns in a single dimension, mining multidimensional sequential patterns can give us more informative and useful patterns. Due to the huge increase in data volume and also quite large search space, efficient solutions for finding patterns in multidimensional sequence data are nowadays very important. For this reason, developing a parallel algorithm is necessary. In this paper, we present a multidimensional sequence model and a parallel algorithm follows the level-wise approach and all participating processors or workers generate candidate sequence and count their supports independently. Simulation experiments show good load balancing and scalable and acceptable speedup over different processors and problem sizes.
Index Terms—Data Mining, Sequential Patterns, Sequence Data, Parallel Algorithms.
Mahdi Esmaeili is with the Department of Computer science, Islamic Azad University (Kashan branch), Kashan, Iran (phone: +98-361-5550055; fax: +98-361-5550056; e-mail: M.Esmaeili@iaukashan.ac.ir).
Mansour Tarafdar is MS student in Islamic Azad University (Qazvinbranch), Qazvin, Iran (e-mail: firstname.lastname@example.org).
Cite: Mahdi Esmaieli and Mansour Tarafdar, "Sequential Pattern Mining from Multidimensional Sequence Data in Parallel," International Journal of Computer Theory and Engineering vol. 2, no. 5, pp. 730-733, 2010.