Data Pre-processing Techniques in Data Mining

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Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?

Singular Value Decomposition (SVD)

Aggregates

Wavelet transform

Regression models (correct)

What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?

Standardising

Discretisation

Normalisation

Principal Component Analysis (PCA) (correct)

What is the main purpose of feature selection in data pre-processing?

To increase dimensionality

To decrease data accuracy

To introduce redundancy

To eliminate redundant features (correct)

Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?

Filter approaches (A) Signup and view all the answers

Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?

CART (D) Signup and view all the answers

Why do data mining algorithms usually work better if the dimensionality of the data is lower?

To reduce noise and improve efficiency (A) Signup and view all the answers

What is the purpose of standardisation in data pre-processing?

To transform the data to fall within specific ranges (D) Signup and view all the answers

In data transformations, what is the main reason for applying normalisation?

To put data on a common scale like [-1, 1] or [0, 1] (C) Signup and view all the answers

Why is it important to put variables on similar scales during data pre-processing?

To prevent bias due to measurement units affecting the results (A) Signup and view all the answers

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 (B) Signup and view all the answers

Which transformation technique helps to maintain the validity of results while making them more useful?

Discretization (C) Signup and view all the answers

How can different measurement units impact data analysis according to the text?

They can cause different outcomes in the analysis due to variable scales (B) Signup and view all the answers

What is the primary purpose of transforming variables by centring?

To put variables on similar scales (D) Signup and view all the answers

When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?

To achieve homogeneity of data (C) Signup and view all the answers

Which statement best describes why mathematical transformations are used?

To improve the interpretability of variable scales (B) Signup and view all the answers

In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?

Creating consistency in the data spread (C) Signup and view all the answers

When considering data transformation, what should be done if it is not necessary to transform the data?

Avoid transforming the data unnecessarily (C) Signup and view all the answers

What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?

Employing non-parametric methods instead (A) Signup and view all the answers

Data Pre-processing Techniques in Data Mining

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Questions and Answers

Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?

What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?

What is the main purpose of feature selection in data pre-processing?

Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?

Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?

Why do data mining algorithms usually work better if the dimensionality of the data is lower?

What is the purpose of standardisation in data pre-processing?

In data transformations, what is the main reason for applying normalisation?

Why is it important to put variables on similar scales during data pre-processing?

What is the reason for removing size effects and giving all variables equal weight during transformations?

Which transformation technique helps to maintain the validity of results while making them more useful?

How can different measurement units impact data analysis according to the text?

What is the primary purpose of transforming variables by centring?

When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?

Which statement best describes why mathematical transformations are used?

In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?

When considering data transformation, what should be done if it is not necessary to transform the data?

What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?

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Data Pre-processing Techniques in Data Mining

Choose a study mode

Podcast

Questions and Answers

Which method replaces data with a smaller representation, such as parameters using parametric methods like regression models?

What technique helps obtain a compressed representation of the original data by reducing the dimensionality of a dataset?

What is the main purpose of feature selection in data pre-processing?

Which approach to feature selection selects features before running the data mining algorithm using an approach independent of the task?

Which method uses the target data mining algorithm as a black box to find the best subset of attributes for feature selection?

Why do data mining algorithms usually work better if the dimensionality of the data is lower?

What is the purpose of standardisation in data pre-processing?

In data transformations, what is the main reason for applying normalisation?

Why is it important to put variables on similar scales during data pre-processing?

What is the reason for removing size effects and giving all variables equal weight during transformations?

Which transformation technique helps to maintain the validity of results while making them more useful?

How can different measurement units impact data analysis according to the text?

What is the primary purpose of transforming variables by centring?

When normalising data for methods like Neural network and Clustering, what is a key reason for this transformation?

Which statement best describes why mathematical transformations are used?

In the context of reasons for mathematical transformations, what does it mean to 'improve homogeneity of data'?

When considering data transformation, what should be done if it is not necessary to transform the data?

What is suggested as a better alternative to arbitrary and uninterpretable results when transforming data?

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