dynamic itemset counting and implication rules for market basket data
Download
Skip this Video
Download Presentation
Dynamic Itemset Counting and Implication Rules for Market Basket Data

Loading in 2 Seconds...

play fullscreen
1 / 15

Dynamic Itemset Counting and Implication Rules for Market Basket Data - PowerPoint PPT Presentation


  • 91 Views
  • Uploaded on

Dynamic Itemset Counting and Implication Rules for Market Basket Data. Abstract. new algorithm fewer passes fewer candidate itemsets implication rules normalized based on both the antecedent and the consequent truly implications (not co-occurrence) more useful, intuitive results.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Dynamic Itemset Counting and Implication Rules for Market Basket Data' - ross


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
abstract
Abstract
  • new algorithm
    • fewer passes
    • fewer candidate itemsets
  • implication rules
    • normalized based on both the antecedent and the consequent
    • truly implications (not co-occurrence)
    • more useful, intuitive results
apriori vs dic
Apriori vs. DIC
  • Apriori
    • level-wise
    • many passes
  • DIC
    • reduce the number of passes
    • fewer candidate itemsets than sampling
  • example : 40,000 transaction, M = 10,000
counting large itemsets
Counting large itemsets
  • Itemsets : a large lattice
  • count just the minimal small itemsets
    • the itemsets that do not include any other small itemsets
  • mark itemset
    • Solid box - confirmed large itemset
    • Solid circle - confirmed small itemset
    • Dashed box - suspected large itemset
    • Dashed circle - suspected small itemset
dic algorithm
DIC algorithm
  • The empty itemset is marked with a soild box. All the 1-itemsets are marked with dashed circles. All other itemsets are unmarked.
  • Read M transactions. For each transaction, increment the respective counters for the itemsets marked with dashes.
  • If a dashed circle has a count that exceeds the support threshold, turn it into a dashed square. If any immediate superset of it has all of its subsets as solid or dashed squares, add new counter for it and make it dashed circle.
  • If a dashed itemset has beec counted through all the transactions, make it solid and stop counting it.
  • If we are at the end of the transaction file, rewind to the beginning
  • If any dashed itemsets remain, go to step 2.
data structure
Data structure
  • like the hash tree used in Apriori with a little extra information
  • Every node stores
    • the last item in the itemset
    • counter, marker, its state
    • its branches if it is an interior node
implication rules
Implication rules
  • conviction
    • more useful and intuitive measure
    • unlike confidence,
      • normalized based on both the antecedent and the consequent
    • unlike interest,
      • directional
      • actual implication as opposed to co-occurrence
implication rules1
Implication rules
  • support : P(A, B)
  • confidence : P(B|A) = P(A, B)/P(A)
  • interest : P(A, B)/P(A)P(B)
  • conviction : P(A)P(B)/P(A, B)
ad