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.
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- A customer supports a sequence
sifsis 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.
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Boss: What’s your biggest weakness? Me: Honesty. Boss: I don’t consider that a weakness. Me: I don’t give a f*ck what you think. |