![]() 39Yes2Example Cont.Step 2: Mapping intervals/values to consecutive intergers13Intervals for AgeIntegers20. Step 1: Determine the partitions for each quantitative attributes12Intervals for Age20. Find frequent itemsets, whose support is larger than MinSupUse frequent itemsets to generate association rulesPruning out uninteresting rules10ExampleStep 0: Initial set of records11RecordIDAgeMarriedNumCars10023No120025Yes130029No040034Yes250038Yes2Example Cont. Presented by:Sepehr Amir-Mohammadian1OutlineAssociation Rules and Quantitative Association RulesFormal Study of Quantitative Association AnalysisPartitioning Quantitative AttributesIdentifying the Interesting RulesCandidate GenerationConcluding RemarksQ&AĢOutlineAssociation Rules and Quantitative Association RulesFormal Study of Quantitative Association AnalysisPartitioning Quantitative AttributesIdentifying the Interesting RulesCandidate GenerationConcluding RemarksQ&AģAssociation Rules44Boolean Association Rules5TIDABCD1001101200011130011104000010TIDItems100A B D200B C D300A B C400C5Quantitative Association Rules6RecordIDAgeMarriedNumCars10023No120025Yes130029No040034Yes250038Yes26Mapping to Boolean Association Rules7RecordIDAge: 20.29Age: 30.39Married: YesMarried: NoNumCars: 0NumCars: 11001001012001010013001001104000110005000110007Problems8RecordIDAgeMarriedNumCars10023No120025Yes130029No040034Yes250038Yes2Solution9Steps of Proposed ApproachDetermine the number of partitions for each quantitative attributeMap values/ranges to consecutive integer values such that the order is preservedFind the support of each value of the attributes, and combine when support is less than MaxSup. ![]() ![]() March 21, 2013(Slides modified from Sasi Sekhar Kuntas version.) ![]() Mining Quantitative Association Rules in Large Relational DatabasesRamakrishnan SrikantRakesh AgrawalĪCM SIGMOD Conference on Management of Data, 1996 ![]()
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