02-assoc

Frequent items so you know what must be counted

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Unformatted text preview: tems (candidate pairs) Pass 1 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets Pass 2 29 You can use the triangular matrix method with n = number of frequent items May save space compared with storing triples 1/5/2011 Trick: re-number frequent items 1,2,… and keep a table relating new numbers to original item numbers Jure Leskovec, Stanford C246: Mining Massive Datasets 30 For each k, we construct two sets of k-tuples (sets of size k): Ck = candidate k-tuples = those that might be frequent sets (support > s) based on information from the pass for k–1 Lk = the set of truly frequent k-tuples All items C1 1/5/2011 All pairs of items from L1 Count the items Filter L1 Construct Count the pairs C2 Jure Leskovec, Stanford C246: Mining Massive Datasets Filter To be explained L2 Construct C3 31 C1 = all items L1 = those counted on first pass to be frequent C2 = pairs, both elements are frequent (appear in L1) L2 = those in C2 that are frequent (supp ≥ s) In general: Ck = k –tuples, each k –1 of which is in Lk -1 Lk = members of Ck with support ≥ s 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 32 One pass for each k (itemset size) Needs room in main memory to count each candidate k–tuple For typical market-basket data and reasonable support (e.g., 1%), k = 2 requires the most memory 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 33 Observation: In pass 1 of a-priori, most memory is idle We store only individua...
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This document was uploaded on 02/26/2014 for the course CS 246 at Stanford.

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