Mining inter-sequence patterns

0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.10.008 * Corresponding author. Tel.: +886 2 8212 2000 63 E-mail addresses: [email protected], d917250 Sequential pattern and inter-transaction pattern mining have long been important issues in data mining research. The former finds sequential patterns without considering the relationships between transactions in databases, while the latter finds inter-transaction patterns without considering the ordered relationships of items within each transaction. However, if we want to find patterns that cross transactions in a sequence database, called inter-sequence patterns, neither of the above models can perform the task. In this paper, we propose a new data mining model for mining frequent inter-sequence patterns. We design two algorithms, M-Apriori and EISP-Miner, to find such patterns. The former is an Apriori-like algorithm that can mine inter-sequence patterns, but it is not efficient. The latter, a new method that we propose, employs several mechanisms for mining inter-sequence patterns efficiently. Experiments show that EISPMiner is very efficient and outperforms M-Apriori by several orders of magnitude. 2008 Elsevier Ltd. All rights reserved.