Department news:
From 2012, seminar room is changing to a bigger one 0.2.11
Presentation (discussion)
This Friday Saulius will give a presentation.
Title
Mining Sequential Patterns
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.
Attendance: