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picture1_Data Mining Notes 180793 | Lecture6proofs


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File: Data Mining Notes 180793 | Lecture6proofs
LECTURE NOTES 732A75 ADVANCED DATA MINING TDDD41 DATA MINING - CLUSTERING AND ASSOCIATION ANALYSIS ˜ JOSE M. PENA ¨ IDA, LINKOPING UNIVERSITY, SWEDEN 1. Correctness of the Apriori algorithm The ...

icon picture PDF Filetype PDF | Posted on 30 Jan 2023 | 2 years ago

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...Lecture notes a advanced data mining tddd clustering and association analysis jose m pena ida linkoping university sweden correctness of the apriori algorithm proof is not unique you can nd one in article by agrawal srikant available from course website our own alternative be found below weprove induction on k that correct we prove result for then under assumption up to combining this two facts conclude any first recall d minsup input transactional database minimum support output all large itemsets l do ck gen lk generate candidate t c such count cksc return klk trivial case line hypothesis assume now it suces because lines simply frequency candidates thus nothing go wrong there function superset self join i j if call genrules am minconf contradiction rule generation contrary missed let denote missing rules with largest antecedent note wrongly implies has outputted since as proven previous section cannot have when konly called evaluated therefore must subsequent calls e...
								

							

						
	
												
				
					
					
					
			
					
				
				
		
	

	
		





	

	
				

						

						
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										Category:  Uncategorized / Uncategorized 
										
Posted on: 30 Jan 2023 | 2 years ago
										
										
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