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pruefungen:nebenfach:bwl_bin20 [11.08.2020 17:10] – nakamipruefungen:nebenfach:bwl_bin20 [11.08.2020 19:10] (aktuell) – nakami
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-====== Braindump SS 2020======+====== Braindump Business Intelligence SS 2020======
  
 Entnommen aus: https://pad.stuve.fau.de/p/businessintelligencess2020_2 Entnommen aus: https://pad.stuve.fau.de/p/businessintelligencess2020_2
  
 Zeit: 90min; wirkt ausreichend, aber man sollte auf die Uhr gucken. Zeit: 90min; wirkt ausreichend, aber man sollte auf die Uhr gucken.
 +
 Anzahl Seiten: 18(!) Anzahl Seiten: 18(!)
  
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 == 5. SVMs == == 5. SVMs ==
  
 +(SVM x-y-coordinate system with samples from two classes is shown. There is no boundary drawn. Although both classes are mostly separated to either left or right, one sample (which looks like an outlier) is placed in the middle, but is part of one of those classes.)
  
-a) what happens if point x (= one support vector) is erased from data set+a)  What happens if point x (= one support vector) is erased from data set? 
 + 
 +Solution: Boundary travels toward the class which had the outlier.
  
 == 6. Social Media Mining == == 6. Social Media Mining ==
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 (it's not mentioned whether the marketing campaign is intended to result in more direct clicks or clicks through recommendation) (it's not mentioned whether the marketing campaign is intended to result in more direct clicks or clicks through recommendation)
  
-c) difference between centrality and centralization+c) Difference between centrality and centralization 
 + 
 +Solution: Centrality refers to the position of an individual actor. Centralization characterizes the total network.
  
-d) calculate closeness centralization of network+d) Calculate closeness centralization of network
  
-e) argue which network is better, all centrality and centralization measures (closeness centralization is from the last step) and the actual networks were given+e) Argue which network is better! (all centrality and centralization measures (closeness centralization is from the last step) and the actual networks are given)
  
 == 7. Association rules == == 7. Association rules ==
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 10.000 shoes sales were tracked. Left side: Single-shoe-pair occurence (in basket) in percentage. Right side: Two shoe pairings occurence (in basket) in numbers. We are looking at Speedrunner, Endurance and Fighter (names of shoes). 10.000 shoes sales were tracked. Left side: Single-shoe-pair occurence (in basket) in percentage. Right side: Two shoe pairings occurence (in basket) in numbers. We are looking at Speedrunner, Endurance and Fighter (names of shoes).
  
-a) Find the four 1-to-1-itemset- association rules ({A} -> {B}) from given data. calculate support & confidence+a) Find the four 1-to-1-itemset- association rules ({A} -> {B}) from given data. calculate support & confidence.
  
-b) describe FP-Growth+b) Describe the steps for the FP-Growth algorithm.