18 Questions
Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?
Regression models
What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?
Principal Component Analysis (PCA)
What is the main purpose of feature selection in data pre-processing?
To eliminate redundant features
Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?
Filter approaches
Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?
CART
Why do data mining algorithms usually work better if the dimensionality of the data is lower?
To reduce noise and improve efficiency
What is the purpose of standardisation in data pre-processing?
To transform the data to fall within specific ranges
In data transformations, what is the main reason for applying normalisation?
To put data on a common scale like [-1, 1] or [0, 1]
Why is it important to put variables on similar scales during data pre-processing?
To prevent bias due to measurement units affecting the results
What is the reason for removing size effects and giving all variables equal weight during transformations?
To ensure all variables have an equal impact on the analysis
Which transformation technique helps to maintain the validity of results while making them more useful?
Discretization
How can different measurement units impact data analysis according to the text?
They can cause different outcomes in the analysis due to variable scales
What is the primary purpose of transforming variables by centring?
To put variables on similar scales
When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?
To achieve homogeneity of data
Which statement best describes why mathematical transformations are used?
To improve the interpretability of variable scales
In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?
Creating consistency in the data spread
When considering data transformation, what should be done if it is not necessary to transform the data?
Avoid transforming the data unnecessarily
What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?
Employing non-parametric methods instead
This quiz covers data pre-processing techniques in data mining, including data transformations and feature weighting to improve the efficiency and accuracy of mining algorithms. Learn how to eliminate irrelevant features, make the data more understandable, and reduce the time and memory requirements of the algorithm.
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