Highly Scalable Sequential Pattern Mining Based on MapReduce Model on the Cloud
Chun‐Chieh Chen, Chi-Yao Tseng, Ming-Syan Chen⋆ · 2013
Sequential pattern mining is an essential data mining technique that has been widely applied to many real world applications. However, traditional algorithms generally suffer from the scalability problem when dealing with big data. In this paper, we aim to significantly upgrade the scale and propose Sequential PAttern Mining algorithm based on MapReduce model on the Cloud (abbreviated as SPAMC). Derived from the prior SPAM algorithm, we design an iterative MapReduce framework to efficiently generate and prune candidate patterns when constructing the lexical sequence tree. This framework not only distributes the sub-tasks of tree construction to independent mappers in parallel, but also enables the parallel processing of support counting. We conduct extensive experiments on the cloud environment of 32 virtual machines with up to 12.8 million transactional sequences. Experimental results show that SPAMC can significantly reduce mining time with big data, achieve extremely high scalability, and provide perfect load balancing on the cloud cluster.