Data Mining Chapter 3
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
...........,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
Data integration is the process of converting data from one format to another.
True
False
Data transformation is the process of converting data from one format to another.
True
False
Data discretization is the process of converting continuous data into discrete intervals.
True
False
Outlier detection is the process of identifying data points that are significantly different from the rest of the data.
True
False
Data sampling is the process of selecting a subset of data from a larger dataset.
True
False
Data normalization is the process of converting data into a standard format.
True
False
Data imputation is the process of identifying and removing missing values from a dataset
True
False
Feature selection is the process of identifying and removing irrelevant or redundant variables from a dataset
True
False
Data smoothing is the process of identifying and removing noise from a dataset.
True
False
Data deduplication is the process of identifying and removing duplicate records from a dataset.
True
False
Data discretization is the process of converting categorical data into numerical data.
True
False
Principal Component Analysis (PCA) is a technique used in data reduction
True
False
Data transformation is the process of scaling data to a specific range.
True
False
Data normalization and standardization are the same thing.
True
False
Data imputation is the process of identifying and handling inconsistent data in a dataset.
True
False
Data reduction is the process of increasing the number of dimensions in a dataset.
True
False
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