Method and system for mining fully weighted positive and negative association patterns between words in text
A fully weighted, positive and negative correlation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as neglect, boredom, and failure to consider fully weighted data
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[0121] Example: In the examples in Table 2, wdR(i 1 )=(0.85+0.93+0.65+0.75) / 1=3.18, wdR(i 2 )=0.61, wdR(i 1 ,i 2 )=(0.93+0.21+0.65+0.35+0.75+0.05) / 2=1.47, awsup(i 1 ,i 2 )=1.47 / 5=0.29,
[0122] Definition 3
[0123] Fully weighted frequent itemsets and negative itemsets: set the minimum support threshold as minsup, and the minimum itemset weight dimension ratio threshold as minwdR, obviously, minwdR=n×minsup, if fully weighted itemsets support awsup(I)≥minsup , or wdR(I)≥minwdR, then the itemset I is a fully weighted frequent itemset; for a fully weighted itemset (I 1 , I 2 ), if its sub-itemset I 1 and I 2 is a frequent itemset, and awsup(I 1 , I 2 )1 , I 2 )1 , I 2 ) is a fully weighted negative itemset.
[0124] Example: Set minsup=0.1, then minwdR=5×0.1=0.5, as can be seen from the above example, wdR(i 1 ,i 2 )=1.47>minwdR, so, (i 1 ,i 2 ) is a fully weighted frequent itemset; wdR(i 1 )=3.18>minwdR, wdR(i 4 )=0.96>minwdR, wdR(i 1 ,i 4 )=0.381 ,i ...
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