7.00.02 - Background - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
September 2017
English (United States)
Last Update

In a sequential pattern mining application, each sequence is an ordered list of item sets, and each item set contains at least one item. Items within a set are unordered.

In web clickstream analysis, each set has only one item. In purchase behavior analysis, a set has an item for each item that the customer buys in one shopping session.

In sequential pattern mining, sequence α is a subsequence of sequence β if both of the following are true:

  • Each item set ai in α is a subset of an item set bj in β.
  • The ai elements in α have the same order as the bj elements in β.

More formally: sequence α=<1a2...an> is a subsequence of sequence β=<b1b2...bm>, and β is a super sequence of α, if there exist integers 1≤j1<j2<...≤jnm such that a1⊆bj1,a2⊆bj2,...,an⊆bj n.

The support of sequence α in a sequence data set SDB is defined as the number of sequences in SDB that contain α (that is, the number of sequences in SDB that are super sequences of α).

Given sequence data set SDB and threshold T, sequence α is called as a frequent sequential pattern of SDB, if support(α)≥T. The problem of sequential pattern mining is to find all possible frequent sequential patterns, given a sequence data set SDB and a threshold T.