LASH: Large-Scale Sequence Mining with Hierarchies

Abstract

We propose LASH, a scalable, distributed algorithm for mining sequential patterns in the presence of hierarchies. LASH takes as input a collection of sequences, each composed of items from some application-specific vocabulary. In contrast to traditional approaches to sequence mining, the items in the vocabulary are arranged in a hierarchy: both input sequences and sequential patterns may consist of items from different levels of the hierarchy. Such hierarchies naturally occur in a number of applications including mining natural-language text, customer transactions, error logs, or event sequences. LASH is the first parallel algorithm for mining frequent sequences with hierarchies; it is designed to scale to very large datasets. At its heart, LASH partitions the data using a novel, hierarchy-aware variant of item-based partitioning and subsequently mines each partition independently and in parallel using a customized mining algorithm called pivot sequence miner. LASH is amenable to a MapReduce implementation; we propose effective and efficient algorithms for both the construction and the actual mining of partitions. Our experimental study on large real-world datasets suggest good scalability and run-time efficiency.