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Diagonally Subgraphs Pattern Mining

Diagonally Subgraphs Pattern Mining. Moti Cohen Ehud Gudes DMKD ’ 04. Introduction. Hybrid gSpan-DFS Code tree-based FSG – BFS Apriori Reverse Depth Search. V 0. X. a. V 1. c. Y. b. b. V 2. Z. V 3. X. b. V 4. X. Lexicographic Ordering in graphs. Minimum DFS Code.

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Diagonally Subgraphs Pattern Mining

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  1. Diagonally Subgraphs Pattern Mining Moti Cohen Ehud Gudes DMKD’04

  2. Introduction • Hybrid • gSpan-DFS Code tree-based • FSG – BFS Apriori • Reverse Depth Search

  3. V0 X a V1 c Y b b V2 Z V3 X b V4 X Lexicographic Ordering in graphs Minimum DFS Code 01XaY 12YbX 13YbZ 30ZcX 34ZbX

  4. Algorithm • Prefix Based Lattice • Reverse Depth Search

  5. Graph database X Frequent1 edge: xax,xaz,xbx,xby,xbz,ycz

  6. b x c b b c c c b b x z z x x x z z x x x x x x y x x b b c b b b b b b y y y y z z y y y c z xby-prefix tid{1,2,3} F:{xby,xbz,ycz} tid{1,2} tid{1,2} {<c1,ycz>} {<c1,ycz>,<c2,ybx>} Sup:1 Sup:2 Sup:2 Sup:1 {<c1,xby>} tid{1} tid{1,2} tid{1} tid{1,2} Sup:1 Sup:2 Sup:0

  7. Candidates Generation k+1 • Join two frequent k-subgraphs • Contain the same k-1-subgraph as core • Extension(k-1subgraph): {(ksub,extend edge),(),..,()} • Join Ri and R with core Qj • Qj Subpatterns(R) • Ri Extension(Qj)

  8. extension

  9. Freq. Anti-Monotone Pruning • all its k-subgraphs are frequent • Massive computation • Partial FAM • Almost as good as FAM • Drop gk+1candidates • generated times < |subpattern(gk)|

  10. Experiment • Intel 2.0GHz • 256MB winxp • vc++6.0

  11. Synthetic minsup = 1%

  12. Chemical Compound dataset

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