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For i =1 = 1,…,kk Find all frequent sets of size i by composing sets of size ii-1 1 that differ in 1 element Open question: Efficiently find only maximal frequent sets [Agrawa‐Srikant ‘99] 11/8/2010 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 10
[Kumar et al. ‘99] Claim: (3) (itemsets) solves (2) (bipartite graphs) How? View each node i as a set S i of nodes i points to K Ks,t = a set t y of f size i t that occurs in s sets Si Looking for Ks,t set of frequency threshold to s and look at layer t – all frequent sets of size t 11/8/2010 Jure Leskovec, <strong>Stanford</strong> CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 11
Page 1 and 2: CS224W: Social and Information Netw
Page 3 and 4: Today: 3 methods (3) Trawling: Comm
Page 5 and 6: (2) A more well‐defined well defi
Page 7 and 8: Marketbasket analysis: What items
Page 9: U={1,2,3,4,5}, U={12345} f=3 S1={1
Page 13 and 14: For the proof we will need the foll
Page 15 and 16: Note: As soon as s nodes appear in
Page 17 and 18: We have: Total height of all bucket
Page 19 and 20: Undirected graph G(V,E): G(V E): B
Page 21 and 22: A Express partitioning objectives
Page 23 and 24: Criterion: Normalized‐cut [ [Shi
Page 25 and 26: jth j coordinate of Ax: Sum of th
Page 27 and 28: What if G is not connected? Say G
Page 29 and 30: Degree matrix (D): n n diagonal ma
Page 31 and 32: For symmetric matrix M: 2 min x T
Page 33 and 34: Express p p partition ( (A,B) , ) a
Page 35 and 36: How to define a “good” good par
Page 37 and 38: p g Pre‐processing: Build Lapla
Page 39 and 40: 11/8/2010 Jure Leskovec, Stanford C
Page 41 and 42: k‐eigenvector k eigenvector Algor
Page 43 and 44: Eigengap: The difference between t
Page 45: Non‐overlapping Non overlapping v

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