Wang C.-S.Lee A.J.T.2019-07-242019-07-24200909574174https://scholars.lib.ntu.edu.tw/handle/123456789/415123Sequential 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 EISP-Miner is very efficient and outperforms M-Apriori by several orders of magnitude. ? 2008 Elsevier Ltd. All rights reserved.Data miningInter-sequence patternInter-transaction patternSequential patternjournal article10.1016/j.eswa.2008.10.0082-s2.0-60249100705https://www.scopus.com/inward/record.uri?eid=2-s2.0-60249100705&doi=10.1016%2fj.eswa.2008.10.008&partnerID=40&md5=4553b6c50edfa2aa30454932351f16b5