Data Mining Chapter 3

A visually engaging illustration of data mining concepts such as data cleaning, encoding, and transformation, featuring abstract representations of data flows and analytical tools.

Data Mining Essentials Quiz

Test your knowledge on data mining concepts with our comprehensive quiz designed specifically for Chapter 3. Whether you are a student or a professional, this quiz covers fundamental topics in data preparation, including data cleaning, transformation, and encoding.

Key features of the quiz:

47 multiple choice questions
Focus on data mining techniques
Instant feedback on your performance

47 Questions12 MinutesCreated by MiningMaster321

...........,routines work to “clean” the data by filling in missing values, smooth-ing noisy data, identifying or removing outliers, and resolving inconsistencies.

Data integration

Data cleaning

Data mining

None of them

All of them are methods of Missing Values except.

Ignore the tuple:

Use a global constant to fill in the missing value

Fill in the missing value manually

Integration

………, data encoding schemes are applied so as to obtain a reduced or “compressed” representation of the original data.

In dimensionality reduction

Numerosity reduction

A and B

None of them

.........., the data are replaced by alternative, smaller representa-tions using parametric models or nonparametric models

In dimensionality reduction

Numerosity reduction

A and B

None of them

……… methods smooth a sorted data value by consulting its “neighbor- hood,” that is, the values around it.

Smoothing

Binning

Storing

None of them

.......... Says that each value of the given attribute must be different from all other values for that attribute.

A unique rule

A consecutive rule

A null rule

None of them

........says that there can be no miss- ing values between the lowest and highest values for the attribute, and that all values must also be unique.

A unique rule

A consecutive rule

A null rule

None of them

�…..specifies the use of blanks, question marks, special characters, or other strings that may indicate the null condition.

A unique rule

A consecutive rule

A null rule

None of them

...........tools use simple domain knowledge to detect errors and make corrections in the data. These tools rely on parsing and fuzzy matching techniques when cleaning data from multiple sources.

Data cleaning

Data integration

Data scrubbing

Data mining

Which of the following is a common technique used in data cleaning?

Data normalization

Data aggregation

Outlier detection

Data sampling

Which of the following is a technique used to combine data from multiple sources?

Data cleaning

Data integration

Data reduction

Data transformation

Which of the following is a technique used to remove noise from data?

Data sampling

Data smoothing

Data discretization

Data normalization

Which of the following is a technique used in data reduction?

Data sampling

Data normalization

Outlier detection

Data integration

Which of the following is a technique used to convert categorical data into numerical data?

Data normalization

encoding

Data integration

Data transformation

Which of the following is a technique used to identify and remove duplicate records in a dataset?

Data sampling

Data integration

Data normalization

Data deduplication

Which of the following is a technique used to remove missing values from a dataset?

Data imputation

Data normalization

Outlier detection

Data discretization

Which of the following is a technique used to reduce the number of variables in a dataset?

Principal Component Analysis (PCA)

Data normalization

Outlier detection

Data discretization

Which of the following is a technique used to scale data to a specific range?

Data normalization

Data integration

Data transformation

Data discretization

Which of the following is a technique used to transform data into a new representation?

Data sampling

Data transformation

Data integration

Data normalization

Which of the following is a technique used to identify and remove irrelevant or redundant variables in a dataset?

Data normalization

Principal Component Analysis (PCA)

Outlier detection

Feature selection

Which of the following is a technique used to reduce the dimensionality of a dataset?

Principal Component Analysis (PCA)

Data normalization

Data discretization

Outlier detection

Which of the following is a technique used to convert continuous data into discrete intervals?

Data normalization

Data discretization

Data integration

Data transformation

Which of the following is a technique used to standardize data by subtracting the mean and dividing by the standard deviation?

Data normalization

Data discretization

Data transformation

Data integration

Which of the following is a technique used to replace missing values in a dataset with the median value?

Data imputation

Data normalization

Outlier detection

Data discretization

Which of the following is a technique used to identify and handle inconsistent data in a dataset?

Data sampling

Data transformation

Data cleaning

Data imputation

Which of the following is a technique used to reduce the number of dimensions in a dataset while retaining important information?

Data discretization

Data reduction

Data transformation

Data normalization

Which of the following is a technique used to handle missing values in a dataset by predicting the missing values using statistical models?

Data sampling

Data normalization

Outlier detection

Data imputation

Which of the following is a technique used to transform data into a form that is more suitable for machine learning algorithms?

Data normalization

Data discretization

Data integration

Data transformation

Which of the following is a technique used to reduce the number of dimensions in a dataset by transforming the data into a new space with fewer dimensions?

Principal Component Analysis (PCA)

Data normalization

Outlier detection

Data discretization

Which of the following is a technique used to convert categorical data into numerical data by creating a new binary variable for each category?

One-hot encoding

Data discretization

Data sampling

Data transformation

Data cleaning is the process of identifying and correcting or removing inaccurate or irrelevant data from a dataset

True

False

Outlier detection is the process of identifying data points that are significantly different from the rest of the data.

True

False

Feature selection is the process of identifying and removing irrelevant or redundant variables from a dataset

True

False

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