Preprocessing

Question 1

Welche Dimensionen gibt es, um die Qualität von Daten zu messen?

Answer 1

• Completeness: is the data fully available? What to do if not?
• Consistency: differences in data units or name conventions?
• Timeliness: measurements from different epochs?
  Old measure devices?
• Believability: is the data source reliable?
• Interpretability: how easily can the data be understood?

Question 2

Für das Data Cleaning, über welche Typen an Fehlern sollte man Bescheid wissen?

Answer 2

• Incomplete: lacking attribute values, lacking certain attributes of
  interest, or only aggregate data available
• Noisy: containing noise, errors, or outliers
• Inconsistent: containing discrepancies in codes or names
• Intentionally imprecise
  – Jan. 1 as everyone’s birthday

Question 3

Wie kann man mit fehlenden Daten umgehen?

Answer 3

• Ignorieren: kein großer Effekt bei großen Daten
• manuell die Einträge überarbeiten
• automatisch die Einträge überarbeiten (global constant, mean, most probable value using inference such as Bayesian
  formula or decision tree based on other attributess)

Question 4

Was ist Data Integration?

Answer 4

Data integration combines data from
multiple sources into a coherent store

Question 5

Mit welcher Methode kann man redundante Attribute erkennen?

Answer 5

chi-square test (nominal)
correlation analysis

Question 6

Was sind die Vorteile von Data Integration?

Answer 6

• reduce/avoid redundancies and inconsistencies and
• improve mining speed and quality

Question 7

Beschreib den Chi-Square Test mathematisch

Answer 7

Question 8

Was bedeutet ein hohes Chi-Quadrat?

Answer 8

→ data distributions are statistically different

Question 9

Was bedeutet ein niedriges Chi-Quadrat?

Answer 9

distributions are similar

Question 10

Wie funktioniert ChiMerge?

Answer 10

Man hat Intervalle und checkt rekursiv, ob die Verteilung der Label in den beiden ähnlich ist anhand des Chi-Quadrat tests und mergt diese, falls dies stimmt

Question 11

Beschreib den Pearson’s product
moment coefficient mathematisch

Answer 11

Question 12

Pearson’s product moment coefficient

Was bedeutet es, wenn r > 0?

Answer 12

A and B are positively correlated

Question 13

Pearson’s product moment coefficient

Was bedeutet es, wenn r = 0?

Answer 13

uncorrelated, not necessarily independent

Question 14

Pearson’s product moment coefficient

Was bedeutet es, wenn r < 0?

Answer 14

negatively correlated

Question 15

Wie berechent man die Kovarianz?

Answer 15

Question 16

Was bedeutet eine Kovarianz von größer als null?

Answer 16

A and B tend to be together
larger or together smaller than their expected values

Question 17

Was bedeutet eine Kovarianz von kleiner als null?

Answer 17

if A is larger than its expected
value, B is likely to be smaller than its expected value.

Question 18

Wie kann die Kovarianz vereinfacht werden?

Answer 18

Question 20

Wie berechnet man element ij einer Kovarianzmatrix?

Answer 20

it computes the covariance between feature i and feature j

Question 21

Welche Strategien für das Binning existieren?

Answer 21

• equal-width
• equal-first (same number of samples)

Question 22

Welche Smoothing Strategien im Anschluss des Binnings existieren?

Answer 22

Question 23

Welche zwei Wege zur Dimensionsreduzierung existieren?

Answer 23

• Feature selection: A process that chooses an optimal
  subset of features according to an objective function
• Feature extraction: refers to the mapping of the original
  high-dimensional data onto a lower-dimensional space

Question 24

Was minimiert deskriptive Dimensionsreuzierung?

Answer 24

den Informationsverlust

Question 25

Was maximiert prädiktive Dimensionsreduzierung?

Answer 25

die Klassendiskrimination

Question 26

Wie bestimmt man die separation quality?

Answer 26

Question 27

Was ist entity identification, or entity resolution?

Answer 27

Entity identification, or entity resolution, is a common challenge in data integration. It involves identifying and linking records that refer to the same entities (such as individuals, companies, or products) across different data sources. This process can be complex due to variations in how entities are represented (different names, addresses, etc.) in various sources.

Question 28

Which of the following statements on boxplots are correct?

• The whiskers extend to Q1 and Q3.
• It is possible to create a boxplot for ordinal data.
• The box of a boxplot contains half of the data.
• Boxplots show the min, max, Q1, Q3, and mean of a distribution.

Answer 28

• The box of a boxplot contains half of the data.,
• It is possible to create a boxplot for ordinal data.

Question 29

Which of the following statements are correct?
1. Boxplots contain more information than histograms.
2. The area of the bars in a histogram matters.
3. Buckets of histograms can have varying size.
4. In histograms, the buckets can be reordered.

Answer 29

2. The area of the bars in a histogram matters.
3. Buckets of histograms can have varying size.

Question 30

Which of the following statements are correct?
1. Equal width binning results in bins with the same number of elements.
2. Binning is a simple way of data discretization.
3. Equal width binning is not influenced by outliers.
4. Bins can be used for data smoothing.

Answer 30

2 und 4

Question 31

Wie können Bins für data smoothing sorgen?

Answer 31

Data smoothing is a technique used to reduce the noise within a dataset to highlight the underlying trend, and binning is one method to achieve this. By grouping data points into bins, and then replacing each data point by a central value of the bin (like the mean or median), the minor fluctuations or the noise in the data can be smoothed out. This process simplifies the data, making it easier to observe long-term trends and patterns.

Question 32

Which of the following statements about outliers is correct?
1. Graphical outlier detection is suitable for high-dimensional data.
2. Outlier detection can be used to prevent credit card fraud.
3. It is always useful to automatically remove all outliers to reduce noise.

Answer 32

2. Outlier detection can be used to prevent credit card fraud.

Question 33

Which of the statements are not correct?
1. It is never possible to automatically detect redundant attributes.
2. Data integration may lead to redundancy in the dataset.
3. Data integration combines data from multiple sources.
4. Entity identification is a problem in data integration.

Answer 33

1. It is never possible to automatically detect redundant attributes.

Question 34

Wie berechnet man die Expected werte in der confusion tabelle bei chi quadrat

Answer 34