Association Analysis 5 Mining Word Associations

1
Association Analysis (5)(Mining Word
Associations)
2
Mining word associations (in Web)
Document-term matrix Frequency of words in a
document

• Itemset here is a collection of words
• Transactions are the documents.
• Example
• W1 and W2 tend to appear together in the same
  documents.
• Potential solution for mining frequent itemsets
• Convert into 0/1 matrix and then apply existing
  algorithms
• Ok, but looses word frequency information

3
Normalize First

• How to determine the support of a word?
• First, normalize the word vectors
• Each word has a support, which equals to 1.0
• Reason for normalization
• Ensure that the data is on the same scale so that
  sets of words that vary in the same way have
  similar support values.

4
Association between words

• E.g. How to compute a meaningful normalized
  support for W1, W2?
• One might think to sum-up the average normalized
  supports for W1 and W2.
• s(W1,W2)
• (0.40.33)/2 (0.40.5)/2 (0.20.17)/2
• 1
• This result is by no means an accident. Why?
• Averaging is useless here.

5
Min-APRIORI

• Use instead the min value of normalized support
  (frequencies).

Example s(W1,W2) min0.4, 0.33
min0.4, 0.5 min0.2, 0.17 0.9
s(W1,W2,W3) 0 0 0 0 0.17 0.17
6
Anti-monotone property of Support
Example s(W1) 0.4 0 0.4 0 0.2
1 s(W1, W2) 0.33 0 0.4 0 0.17
0.9 s(W1, W2, W3) 0 0 0 0 0.17 0.17
So, standard APRIORI algorithm can be applied.