Slide 4: Sequential pattern mining: Example I (cont.)

Slide 3: Sequential pattern mining: Example I
Slide 5: Sequential pattern mining: Example I (cont.)
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Sequential Pattern Mining: Example I (Cont.)

Some terms are explained next before going further:

An itemset is a nonempty set of items such as {bread, milk}.

A sequence is an ordered list of itemsets such as 〈{milk} {bread, milk}〉.

All the transactions of a customer can together be viewed as a sequence where each transaction corresponds to a set of items and the list of transactions ordered by increasing transaction time corresponds to a sequence. We call such a sequence a customer-sequence.

In a set of sequences a sequence s is maximal if s is not contained in any other sequence.

Customer Id	Customer Sequence
1	{ (30) (90) }
2	{ (10 20) (30) (40 60 70) }
3	{ (30 50 70) }
4	{ (30) (40 70) (90) }
5	{ (90) }

A customer supports a sequence s if s is contained in the customer-sequence for this customer.

The support for a sequence is defined as the fraction of total customers who support this sequence.

Given a database D of customer transactions the problem of mining sequential patterns is to find the maximal sequences among all sequences that have a certain user-specified minimum support. Each such maximal sequence represents a sequential pattern.

A sequence satisfying the minimum support constraint is called a large sequence.