Data Mining and KD at TU München

Flashcards and summaries for Data Mining and KD at the TU München

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ROC - explanation


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


PR - breakeven point


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Linear discriminant Analysis - how to calculate?

Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Median filter, when?

Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


Edit distance


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


Fuzzy clustering, when?


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


ID3, when to use?


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


Principal component analysis - when?


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


Hypercube standardization is appropriate for 


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


Mean and variance standardization is appropriate for


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


How to calculate the principal axis (PCA)


Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:


When does Naive Bayesian Classifier not work?


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Exemplary flashcards for Data Mining and KD at the TU München on StudySmarter:

Data Mining and KD


ROC - explanation


Scatter plot of the true positive rate TPR and the false positive rate FPR

Data Mining and KD


PR - breakeven point


  • The main diagonal of precision recall
  • Important classification criterion
  • High breakeven point = Good classifier 

Data Mining and KD

Linear discriminant Analysis - how to calculate?

  • You want to find wx + b
  • w = mean1 – mean 2
  • b = – w * mean1+mean2/2

Data Mining and KD

Median filter, when?

  • Series data
  • When we have outliers
  • Remove noise

Data Mining and KD


Edit distance


  • The minimum number of edit operations
  • Operations: insert, delete, or change a sequence element

We denote
Lij(x; y) as the edit distance between the first i elements of x and the first j elements of y

Data Mining and KD


Fuzzy clustering, when?


  • Good results, even if the clusters are overlapping and data are noisy
  • Sensitive to outliers.

Outliers are equivalent to other data points that

are equidistant to all data points like the middle point. But intuitively we expect outliers to have low membership

Data Mining and KD


ID3, when to use?


  • Extension of classification and regression tree
  • Accept real-valued and missing features

  • Uses a pruning mechanism to reduce tree size

Data Mining and KD


Principal component analysis - when?


When we want to visualize high-dimensional data

Work with fewer dimensions

Data Mining and KD


Hypercube standardization is appropriate for 


Uniformly distributed features

Data Mining and KD


Mean and variance standardization is appropriate for


Gaussian distributed features.

Data Mining and KD


How to calculate the principal axis (PCA)


  • Calculate mean per feature
  • Calculate covariance matrix
    • 1/n SUM( (x-x_mean)*(y-y_mean) )
  • A-ILambda
  • Get lambda
  • go back to your covariance matrix and make 
    • First row = x11*Lambda
    • Second row = x12* Lambda
    • etc
  • Get a relationship between x11, x12 etc for all different lambdas
  • Normalise them. Divide them on Sqr(a^2 + b^2 …)
  • You now have your principal axes

Data Mining and KD


When does Naive Bayesian Classifier not work?


  • When classes are based on correlation
  • Variance difference
  • Hight -> + Weight -> –
  • Hight -> + Weight -> +

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