Q1.Difference between classification of data and clustering of data, You are required to give your o

Aug 15th, 2016
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Q1.Difference between classification of data and clustering of data, You are required to give your opinion about it with any example of your choice. Q2. How we compute ranking cubes for efficient top-k ranking? Can you give example of one application domain where ranking cubes are used? Q3 Can apriori mining algorithm handle convertible constraints? Justify. Q4. Discuss the relationship between colossal and core patterns. Q5. What is boosting? State why it may improve the accuracy of decision tree induction? Q6. Ensemble methods improve classification accuracy. How?

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Answer--------------1For someone who is new to Data mining, classification and clustering can seem similarbecause both data mining algorithms essentially divide the datasets into sub-datasets; Butthere is difference between them and this blog-post, well see exactly that:CLASSIFICATIONCLUSTERINGWe have a Training setcontaining data that have beenpreviously categorizedBased on this training set, thealgorithms finds the category thatthe new data points belong toWe do not know thecharacteristics of similarity ofdata in advanceUsing statistical concepts, wesplit the datasets into subdatasets such that the Subdatasets have Similar dataSince a Training set exists, wedescribe this technique asSupervised learningSince Training set is not used,we describe this technique asUnsupervised learningExample:We use training datasetwhich categorized customers thathave churned. Now based on thistraining set, we can classifywhether a customer will churn ornot.Example:We use a dataset ofcustomers and split them intosub-datasets of customers withsimilar characteristics. Nowthis information can be used tomarket a product to a specificsegment of customers that hasbeen identified by clusteringalgorithmGive your opinion about it with any example of your choice.-------------------------------------------------Introduces a new mathematical framework for two related classical problems in statisticallearning: data cluster

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