Modul 8:Sequential Pattern Mining
Terminology Item Itemset Sequence (Customer-sequence) Subsequence Support for a sequence Large/frequent sequence
Example
Q. How to find the sequential patterns?
Example
Item
Itemset
Transaction
Example (cont.)
Sequence
3-Sequence
Subsequence
Example (cont.)
<(30) (90)> is supported by customer 1 and 4
<30 (40 70)> is supported by customer 2 and 4
customer 1 and 4 contain <(30) (90)>
Example (cont.)
Q. Find the large/frequent sequences with minimum support set to 25%:
-Frequent sequence = The sequence with minimum support
<(30)>, <(40)>, <(70)>, <(90)><(30) (40)>, <(30) (70)>, <(40 70)>
The Algorithm Apriori Five phases
Sort phase Large itemset phase Transformation phase Sequence phase Maximal phase
Sort the database with customer-id as the major key and transaction-time as the minor key
Sort phase
Find the large itemset. Itemsets mapping
Litemset phase
Transformation phase
Deleting non-large itemsets Mapping large itemsets to integers
Sequence phase Use the set of litemsets to find the desired sequence. Two families of algorithms:
Count-all:
Algorithm AprioriAll Count-some:
Algorithm AprioriSome, Algorithm DynamicSome
AprioriAll The basic method to mine sequential patterns Based on the Apriori algorithm. Count all the large sequences, including non-maximal
sequences. Use Apriori-generate function to generate candidate
sequence.
AprioriAll (cont.)
L1 = {large 1-sequences}; // Result of the phasefor ( k=2; Lk-1≠Φ; k++) do begin Ck = New candidate generate from Lk-1 foreach customer-sequence c in the database do Increment the count of all candidates in Ck that are contained in cLk = Candidates in Ck with minimum support.EndAnswer=Maximal Sequences in UkLk;
Apriori Candidate Generation
generate candidates for pass using only the large sequences found in the previous pass and then makes a pass over the data to find their support.
Algorithm: Lk the set of all large k-sequences
Ck the set of candidate k-sequences
Apriori Candidate Generation
insert into Ck
select p.litemset1, p.litemset2,…, p.litemsetk-1, q.litemsetk-1
from Lk-1 p, Lk-1 qwhere p.litemset1=q.litemset1,…, p.litemsetk-2=q.litemsetk-2;
forall sequences cCk do forall (k-1)-subsequences s of c do if (sLk-1) then delete c from Ck;
Example: Transformed Customer Sequences
Apriori Candidate Generation
<{1 5}{2}{3}{4}><{1}{3}{4}{3 5}><{1}{2}{3}{4}>
<{1}{3}{5}><{4}{5}>
next step: find the large 1-sequences
With minimum set to 25%
next step: find the large 2-sequences
Sequence Support
<1>
<2>
<3>
<4>
<5>
<{1 5}{2}{3}{4}><{1}{3}{4}{3 5}><{1}{2}{3}{4}>
<{1}{3}{5}><{4}{5}>
ExampleLarge 1-Sequence
4
2
4
4
2
next step: find the large 3-sequences
Sequence Support
<1 2> 2
<1 3> 4
<1 4> 3
<1 5> 3
<2 3> 2
<2 4> 2
<3 4> 3
<3 5> 2
<4 5> 2
<{1 5}{2}{3}{4}><{1}{3}{4}{3 5}><{1}{2}{3}{4}>
<{1}{3}{5}><{4}{5}>
ExampleLarge 2-Sequence
next step: find the large 4-sequences
Sequence Support
<1 2 3> 2
<1 2 4> 2
<1 3 4> 3
<1 3 5> 2
<2 3 4> 2
<{1 5}{2}{3}{4}><{1}{3}{4}{3 5}><{1}{2}{3}{4}>
<{1}{3}{5}><{4}{5}>
ExampleLarge 3-Sequence
next step: find the maximal sequential pattern
Sequence Support
<1 2 3 4> 2<{1 5}{2}{3}{4}><{1}{3}{4}{3 5}><{1}{2}{3}{4}>
<{1}{3}{5}><{4}{5}>
ExampleLarge 4-Sequence
Maximal phase Find the maximum sequences among the set of large
sequences. In some algorithms, this phase is combined with the
sequence phase.
Maximal phase Algorithm:
S the set of all litemsets n the length of the longest sequence
for (k = n; k > 1; k--) do foreach k-sequence sk do Delete from S all subsequences of sk
Sequence Support
<1 2 3 4> 2
Example
Sequence Support
<1> 4
<2> 2
<3> 4
<4> 4
<5> 2
Sequence Support
<1 2> 2
<1 3> 4
<1 4> 3
<1 5> 3
<2 3> 2
<2 4> 2
<3 4> 3
<3 5> 2
<4 5> 2
Sequence Support
<1 2 3> 2
<1 2 4> 2
<1 3 4> 3
<1 3 5> 2
<2 3 4> 2
Find the maximal large sequences
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Examples of Sequence DataSequence Database
Sequence Element (Transaction)
Event(Item)
Customer Purchase history of a given customer
A set of items bought by a customer at time t
Books, diary products, CDs, etc
Web Data Browsing activity of a particular Web visitor
A collection of files viewed by a Web visitor after a single mouse click
Home page, index page, contact info, etc
Event data History of events generated by a given sensor
Events triggered by a sensor at time t
Types of alarms generated by sensors
Genome sequences
DNA sequence of a particular species
An element of the DNA sequence
Bases A,T,G,C
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Examples of Sequence
Web sequence:
< {Homepage} {Electronics} {Digital Cameras} {Canon Digital Camera} {Shopping Cart} {Order Confirmation} {Return to Shopping} >
Sequence of initiating events causing the nuclear accident at 3-mile Island:(http://stellar-one.com/nuclear/staff_reports/summary_SOE_the_initiating_event.htm)
< {clogged resin} {outlet valve closure} {loss of feedwater} {condenser polisher outlet valve shut} {booster pumps trip} {main waterpump trips} {main turbine trips} {reactor pressure increases}>
Sequence of books checked out at a library:<{Fellowship of the Ring} {The Two Towers} {Return of the King}>
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GSP algorithm
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Candidate generation
Contains 2 phase: Join phase and Prune phase
Join phase: Ck = Fk-1 x Fk-1
A sequence s1 and s2 in Fk-1 can be joined if the subsequence obtained by dropping the first item of s1 is the same as the subsequence obtained by dropping the last item of s2.
The resulting sequence is the sequence s1 extended by the last item in s2. The added item becomes a separate element if it was a separate
element in s2, and part of element s1 otherwise
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Candidate Generation Examples
Merging the sequences w1=<{1} {2 3} {4}> and w2 =<{2 3} {4 5}> will produce the candidate sequence < {1} {2 3} {4 5}> because the last two events in w2 (4 and 5) belong to the same element
Merging the sequences w1=<{1} {2 3} {4}> and w2 =<{2 3} {4} {5}> will produce the candidate sequence < {1} {2 3} {4} {5}> because the last two events in w2 (4 and 5) do not belong to the same element
We do not have to merge the sequences w1 =<{1} {2 6} {4}> and w2 =<{1} {2} {4 5}> to produce the candidate < {1} {2 6} {4 5}> because if the latter is a viable candidate, then it can be obtained by merging w1 with < {1} {2 6} {5}>
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Pruning phase: Delete candidate sequences that have an infrequent (k-1)-
subsequence.
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GSP Example
< {1} {2} {3} >< {1} {2 5} >< {1} {5} {3} >< {2} {3} {4} >< {2 5} {3} >< {3} {4} {5} >< {5} {3 4} >
< {1} {2} {3} {4} >< {1} {2 5} {3} >< {1} {5} {3 4} >< {2} {3} {4} {5} >< {2 5} {3 4} >
< {1} {2 5} {3} >
Frequent3-sequences
CandidateGeneration
CandidatePruning
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Database Example
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The mining result
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