02-assoc

# 152011 jure leskovec stanford c246 mining massive

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Unformatted text preview: Finding communities in large graphs (e.g., web) Baskets = nodes; items = outgoing neighbors Searching for complete bipartite subgraphs Ks,t of a big graph … s nodes … t nodes A dense 2-layer graph Use this to define topics: What the same people on the left talk about on the right 1/5/2011 How? View each node i as a bucket Bi of nodes i it points to Ks,t = a set Y of size t that occurs in s buckets Bi Looking for Ks,t set of support s and look at layer t – all frequent sets of size t Jure Leskovec, Stanford C246: Mining Massive Datasets 11 Define: Frequent Itemsets Association rules: Confidence, Support, Interestingness 2 algorithms for finding frequent itemsets: A-priori algorithm PCY algorithm +2 refinements: Multistage Algorithm Multihash Algorithm Random sampling and SON algorithms 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 12 Association Rules: If-then rules about the contents of baskets {i1, i2,…,ik} → j means: “if a basket contains all of i1,…,ik then it is likely to contain j” Confidence of this association rule is the probability of j given I = {i1,…,ik} 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 13 Not all high-confidence rules are interesting The rule X → milk may have high confidence for many itemsets X, because milk is just purchased very often (independent of X) Interest of an association rule I → j: difference between its confidence and the fraction of baskets that contain j Interesting rules are t...
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## This document was uploaded on 02/26/2014 for the course CS 246 at Stanford.

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