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Understanding Sequential Patterns and the GSP Algorithm by Joe Casabona

This article explores the concept of sequential patterns, which are relevant sequences in data where order matters. Unlike traditional association rules, sequential patterns can reveal insights such as customer buying sequences or linguistic structures in text. We discuss the Generalized Sequential Pattern (GSP) algorithm that identifies frequent sequences by progressively increasing their length through candidate generation. Key steps include finding frequent sets, generating candidates, and the possibilities for rule generation based on confidence. This is essential in data mining for uncovering valuable insights.

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Understanding Sequential Patterns and the GSP Algorithm by Joe Casabona

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  1. SEQUENTIAL PATTERNS & THE GSP ALGORITHM BY: JOE CASABONA

  2. INTRO • What are Sequential Patterns? • Why don't ARs suffice? • The General Sequential Pattern Algorithm • Finding Frequent Sets • Candidate Generation • Rule Generation

  3. WHATARE SEQUENTIAL PATTERNS? "Finding statistically relevant patterns between data examples where the values are delivered in a sequence." [3] Very similar to Association Rules, but sequence in this case matters. There may be times when order is important. 

  4. SEQUENTIAL PATTERN EXAMPLES In Transaction Processing:      Do customers usually buy a new controller or a game first after buying an Xbox? In Text Mining:     Order of the words important for finding linguistic or language patterns [1]

  5. OBJECTIVE Given a set S of input data sequences, find all sequences that have a user-specified minimum support. This is called a 'frequent sequence' or sequential pattern. [1] We will use the Generalized Sequential Pattern Algorithm (GSP)

  6. GSP Similar to Apriori Algorithm •  Find individual items with minSupport (1-sequences) • Use them to find 2-sequences • Continue using k-sequences to find (k+1)-sequences • Stop when there are no more frequent sequences. Difference is in Candidate Generation 

  7. GSP: CANDIDATE GENERATION Input : Frequent Set k-1 (F[k-1]) Output: Candidate Set C[k] How it works: • Join F[k-1] with F[k-1] •  Get rid of infrequent sequences (prune) • Note: Order of items matter 

  8. CANDIDATE EXAMPLE F[3] = <{1, 2} {4}>, <{1, 2} {5}>, <{1} {4, 5}>, <{1, 4} {6}>, <{2} {4, 5}>, <{2} {4} {6}> After Join: <{1, 2} {4, 5}>, <{1, 2} { 4} {6}> After Prune: <{1, 2} {4, 5}>  C[4]=  <{1, 2} {4, 5}>

  9. RULE GENERATION Objective not to generate rules, but it can be done.  Sequential Rule: Apply confidence to  Frequent Sequences Label Sequential Rules: Replace some elements in X with *

  10. RERERENCES [1] The Book I am using:  Liu, Bing. Web Data Mining, Chapter 2: Association Rules and Sequential Patterns. Springer, December, 2006  Wikipedia: [2] "GSP Algorithm." http://en.wikipedia.org/wiki/GSP_AlgorithmJune 3, 2008 [3] "Sequence Mining." http://en.wikipedia.org/wiki/Sequence_miningOct. 30, 2008

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