Cross-language query post-translation consequent extension method based on complete weighted positive and negative mode
A fully weighted, extended method technology, applied in cross-language information retrieval query expansion, Internet information retrieval field
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[0095] Example: If C k =(t 1 ∪t 2 ∪t 3 ∪t 4 ) (support degree is 0.65), its single item t 1 , t 2 , t 3 and t 4 The support degrees of are 0.82, 0.45, 0.76 and 0.75 respectively, and its 2_sub-itemset and 3_sub-itemset (t 1 ∪t 2 ), (t 1 ∪t 3 ), (t 1 ∪t 4 ), (t 2 ∪t 3 ), (t 2 ∪t 4 ), (t 1 ∪t 2 ∪t 3 ), (t 1 ∪t 2 ∪t 4 ), (t 2 ∪t 3 ∪t 4 ) support degrees are 0.64, 0.78, 0.75, 0.74, 0.67, 0., 66, 0.56, 0.43 respectively, then the single item with the largest support degree (value 0.82) is t 1 , the sub-itemset with the largest support (value 0.78) in its 2_subitemset and 3_subitemset is (t 1 ∪t 3 ), then use formula (14) to calculate the positive itemset (t 1 ∪t 2 ∪t 3 ∪t 4 ) is 0.81. Its calculation process is as follows:
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[0097] 4. Improvement degree of fully weighted association rules
[0098] The limitation of the traditional association rule evaluation framework (support-confidence) is that it ignores the itemset support in the ru...
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