1 / 10

S EQUENTIAL  P ATTERNS & THE GSP A LGORITHM

S EQUENTIAL  P ATTERNS & THE GSP A LGORITHM. BY : J OE C ASABONA. I NTRO. What are Sequential Patterns? Why don't ARs suffice? The General Sequential Pattern Algorithm Finding Frequent Sets Candidate Generation Rule Generation. W HAT ARE S EQUENTIAL P ATTERNS ?.

awen
Download Presentation

S EQUENTIAL  P ATTERNS & THE GSP A LGORITHM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related