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Mining Sequential Patterns
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Proceedings of the Eleventh International Conference on Data Engineering table of contents
Pages: 3 - 14  
Year of Publication: 1995
ISBN:0-8186-6910-1
Authors
Rakesh Agrawal
Ramakrishnan Srikant
Publisher
IEEE Computer Society  Washington, DC, USA
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ABSTRACT

We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction.


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INDEX TERMS

Keywords:
AprioriAll, AprioriSome, algorithms, customer transactions, customer-ID, knowledge acquisition, large database, pattern matching, retail data processing, scale-up properties, sequential pattern mining, transaction time, very large databases

Collaborative Colleagues:
Rakesh Agrawal: colleagues
Ramakrishnan Srikant: colleagues

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