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Sequential Pattern Mining

Sequential Pattern Mining. COMP 790-90 Seminar BCB 713 Module Spring 2011. Sequential Pattern Mining. Why sequential pattern mining? GSP algorithm FreeSpan and PrefixSpan Boarder Collapsing Constraints and extensions. Sequence Databases and Sequential Pattern Analysis.

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Sequential Pattern Mining

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  1. Sequential Pattern Mining COMP 790-90 Seminar BCB 713 Module Spring 2011

  2. Sequential Pattern Mining • Why sequential pattern mining? • GSP algorithm • FreeSpan and PrefixSpan • Boarder Collapsing • Constraints and extensions

  3. Sequence Databases and Sequential Pattern Analysis • (Temporal) order is important in many situations • Time-series databases and sequence databases • Frequent patterns  (frequent) sequential patterns • Applications of sequential pattern mining • Customer shopping sequences: • First buy computer, then CD-ROM, and then digital camera, within 3 months. • Medical treatment, natural disasters (e.g., earthquakes), science & engineering processes, stocks and markets, telephone calling patterns, Weblog click streams, DNA sequences and gene structures

  4. What Is Sequential Pattern Mining? • Given a set of sequences, find the complete set of frequent subsequences A sequence : < (ef) (ab) (df) c b > A sequence database An element may contain a set of items. Items within an element are unordered and we list them alphabetically. <a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)> Given support thresholdmin_sup =2, <(ab)c> is a sequential pattern

  5. Challenges on Sequential Pattern Mining • A huge number of possible sequential patterns are hidden in databases • A mining algorithm should • Find the complete set of patterns satisfying the minimum support (frequency) threshold • Be highly efficient, scalable, involving only a small number of database scans • Be able to incorporate various kinds of user-specific constraints

  6. Seq. ID Sequence 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 <a(bd)bcb(ade)> A Basic Property of Sequential Patterns: Apriori • A basic property: Apriori (Agrawal & Sirkant’94) • If a sequence S is not frequent • Then none of the super-sequences of S is frequent • E.g, <hb> is infrequent  so do <hab> and <(ah)b> Given support thresholdmin_sup =2

  7. Basic Algorithm : Breadth First Search (GSP) • L=1 • While (ResultL != NULL) • Candidate Generate • Prune • Test • L=L+1

  8. Seq. ID Sequence 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 <a(bd)bcb(ade)> Finding Length-1 Sequential Patterns • Initial candidates: all singleton sequences • <a>, <b>, <c>, <d>, <e>, <f>, <g>, <h> • Scan database once, count support for candidates min_sup =2

  9. Seq. ID Sequence Cand. cannot pass sup. threshold 5th scan: 1 cand. 1 length-5 seq. pat. <(bd)cba> 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> Cand. not in DB at all <abba> <(bd)bc> … 4th scan: 8 cand. 6 length-4 seq. pat. 30 <(ah)(bf)abf> 3rd scan: 46 cand. 19 length-3 seq. pat. 20 cand. not in DB at all <abb> <aab> <aba> <baa><bab> … 40 <(be)(ce)d> 2nd scan: 51 cand. 19 length-2 seq. pat. 10 cand. not in DB at all 50 <a(bd)bcb(ade)> <aa> <ab> … <af> <ba> <bb> … <ff> <(ab)> … <(ef)> 1st scan: 8 cand. 6 length-1 seq. pat. <a> <b> <c> <d> <e> <f> <g> <h> The Mining Process min_sup =2

  10. Generating Length-2 Candidates 51 length-2 Candidates Without Apriori property, 8*8+8*7/2=92 candidates Apriori prunes 44.57% candidates

  11. Pattern Growth (prefixSpan) • Prefix and Suffix (Projection) • <a>, <aa>, <a(ab)> and <a(abc)> are prefixes of sequence <a(abc)(ac)d(cf)> • Given sequence <a(abc)(ac)d(cf)>

  12. Example An Example ( min_sup=2):

  13. PrefixSpan (the example to be continued) Step1: Find length-1 sequential patterns; <a>:4, <b>:4, <c>:4, <d>:3, <e>:3, <f>:3 support pattern Step2: Divide search space; six subsets according to the six prefixes; Step3: Find subsets of sequential patterns; By constructing corresponding projected databases and mine each recursively.

  14. Example to be continued

  15. Example • Find sequential patterns having prefix <a>: • Scan sequence database S once. Sequences in S containing <a> are projected w.r.t <a> to form the <a>-projected database. • Scan <a>-projected database once, get six length-2 sequential patterns having prefix <a> : • <a>:2 , <b>:4, <(_b)>:2, <c>:4, <d>:2, <f>:2 • <aa>:2 , <ab>:4, <(ab)>:2, <ac>:4, <ad>:2, <af>:2 • Recursively, all sequential patterns having prefix <a> can be further partitioned into 6 subsets. Construct respective projected databases and mine each. • e.g. <aa>-projected database has two sequences : • <(_bc)(ac)d(cf)> and <(_e)>.

  16. PrefixSpan Algorithm Main Idea: Use frequent prefixes to divide the search space and to project sequence databases. only search the relevant sequences. PrefixSpan(, i, S|) • Scan S| once, find the set of frequent items b such that • b can be assembled to the last element of  to form a sequential pattern; or • <b> can be appended to  to form a sequential pattern. • For each frequent item b, appended it to  to form a sequential pattern ’, and output ’; • For each ’, construct ’-projected database S|’, and call PrefixSpan(’, i+1,S|’).

  17. Approximate match Compatibility Matrix • When you observe d1 • Spread count as • d1: 90%, d2: 5%, d3: 5%

  18. Match • The degree to which pattern P is retained/reflected in S • M(P,S) = P(P|S)= C(p,s) when when lS=lP • M(P,S) = max over all possible when lS>lP • Example

  19. Calculate Max over all • Dynamic Programming • M(p1p2..pi, s1s2…sj)= Max of • M(p1p2..pi-1, s1s2…sj-1) * C(pi,sj) • M(p1p2..pi, s1s2…sj-1) • O(lP*lS) • When compatibility Matrix is sparse O(lS)

  20. Match in D • Average over all sequences in D

  21. Spread of match • If compatibility matrix is identity matrix • Match = support

  22. Anti-Monotone • The match of a pattern P in a symbol sequence S is less than or equal to the match of any subpattern of P in S • The match of a pattern P in a sequence database D is less than or equal to the match of any subpattern of P in D • Can use any support based algorithm • More patterns match so require efficient solution • Sample based algorithms • Border collapsing of ambiguous patterns

  23. Chernoff Bound • Given sample size=n, range R, • with probability 1- • true value:   •  = sqrt([R2ln(1/)]/2n) • Distribution free • More conservative • Sample size : fit in memory • Restricted spread : • For pattern P= p1p2..pL • R=min (match[pi]) for all 1  i L

  24. Algorithm • Scan DB: O(N*min (Ls*m, Ls+m2)) • Find the match of each individual symbol • Take a random sample of sequences • Identify borders that embrace the set of ambiguous patterns O(mLp * |S| * Lp * n) • Min_match   • existing methods for association rule mining • Locate the border of frequent patterns • in the entire DB • via border collapsing

  25. Border Collapsing • If memory can not hold the counters of all ambiguous patterns • Probe-and-collapse : binary search • Probe patterns with highest collapsing power until memory is filled • If memory can hold all patterns up to the 1/x layer • the space of ambiguous patterns can be narrowed to at least 1/x of the original one • where x is a power of 2 • If it takes a level-wise search y scans of the DB, only O(logxy) scans are necessary when the border collapsing technique is employed

  26. Periodic Pattern • Full periodic pattern • ABCABCABC • Partial periodic pattern • ABC ADC ACC ABC • Pattern hierarchy • ABC ABC ABC DE DE DE DE ABC ABC ABC DE DE DE DE ABC ABC ABC DE DE DE DE

  27. Periodic Pattern • Recent Achievements • Partial Periodic Pattern • Asynchronous Periodic Pattern • Meta Pattern • InfoMiner/InfoMiner+/STAMP

  28. Clustering Sequential Data • CLUSEQ • ApproxMAP

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