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Questions and Answers
What is the term used to describe a collection of data objects and their attributes?
Which term refers to a property or characteristic of an object?
What are attribute values?
In what way can the same attribute be mapped to different attribute values?
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What is the term for ID numbers, eye color, and zip codes in the context of types of attributes?
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What type of attribute includes rankings, grades, and categories like 'tall', 'medium', and 'short'?
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Which technique aims to reduce the number of attributes or objects, change the scale, and create more stable data?
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Which technique involves creating new attributes that capture important information more efficiently than the original attributes?
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What is the major issue when merging data from different sources?
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Which technique aims to avoid the curse of dimensionality and reduce time and memory requirements?
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What technique involves converting continuous attributes into ordinal attributes?
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Which technique involves eliminating data objects, estimating missing values, or ignoring them during analysis?
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Which attribute type has both meaningful differences and ratios?
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What distinguishes continuous attributes from discrete attributes?
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Which type of data involves a set of items purchased during a single transaction?
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What can negatively impact data processing efforts?
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What are examples of reasons for missing values?
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Why is detecting and addressing data quality problems crucial?
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What is the range in which the dissimilarity measure often falls?
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What is the formula for Euclidean Distance in a n-dimensional space?
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What term is used to describe a collection of attributes and their values for a particular object?
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Which term refers to the property or characteristic of an object that can be measured or observed?
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What is the term used to describe the numbers or symbols assigned to an attribute for a particular object?
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Which term is used to describe the property of an attribute that can be different from the properties of the values used to represent the attribute?
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What distinguishes nominal attributes from ordinal attributes?
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What is the term used to describe the collection of attributes that describe an object?
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Which technique aims to reduce the number of attributes or objects, change the scale, and create more stable data?
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What is the term for the phenomenon when data becomes increasingly sparse as dimensionality increases, making density and distance definitions less meaningful?
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What technique involves converting continuous attributes into ordinal attributes and binarizing attributes into binary variables?
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Which type of sampling involves selecting a sample without putting the selected unit back into the population?
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What is the major issue when merging data from different sources and involves identifying and removing duplicate data?
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What technique is used for dimensionality reduction and aims to avoid the curse of dimensionality, reduce time and memory requirements, and eliminate irrelevant features or reduce noise?
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What distinguishes ratio attributes from interval attributes?
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Which type of data can represent molecular structures or webpages?
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What can be a reason for missing values in a dataset?
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What is an example of noise in a dataset?
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What is an example of an outlier in a dataset?
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What type of attribute only distinguishes values without ordering objects?
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What is the formula for Euclidean Distance in a n-dimensional space?
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Study Notes
Data Types and Quality Issues
- S.S. Stevens' categorization of attribute types includes nominal, ordinal, interval, and ratio attributes, each with distinct properties and operations.
- Nominal attributes only distinguish values, while ordinal attributes also order objects, interval attributes have meaningful differences, and ratio attributes have both meaningful differences and ratios.
- Discrete attributes have a finite or countably infinite set of values, while continuous attributes have real numbers as attribute values.
- Data can be represented as record data, transaction data, graph data, or ordered data, each with specific characteristics and examples.
- Record data consists of a fixed set of attributes for each record, while transaction data involves a set of items purchased during a single transaction.
- Graph data can represent molecular structures or webpages, while ordered data includes spatial, temporal, and sequential data, as well as genetic sequence data.
- Data quality issues such as noise, outliers, wrong data, fake data, and missing values can negatively impact data processing efforts.
- Noise can refer to extraneous objects or modification of original values, while outliers are data objects considerably different from most others in the dataset.
- Examples of noise and outliers include distortion of a person's voice on a poor phone line and credit card fraud for noise, and intrusion detection for outliers.
- Reasons for missing values include information not being collected or attributes not being applicable to all cases.
- Data mining examples, such as building a classification model for detecting loan risks using poor data, can lead to credit-worthy candidates being denied loans and more loans being given to individuals that default.
- Detecting and addressing data quality problems, such as noise, outliers, wrong data, fake data, and missing values, is crucial for ensuring accurate data analysis and decision-making.
Data Types and Quality Issues
- S.S. Stevens' categorization of attribute types includes nominal, ordinal, interval, and ratio attributes, each with distinct properties and operations.
- Nominal attributes only distinguish values, while ordinal attributes also order objects, interval attributes have meaningful differences, and ratio attributes have both meaningful differences and ratios.
- Discrete attributes have a finite or countably infinite set of values, while continuous attributes have real numbers as attribute values.
- Data can be represented as record data, transaction data, graph data, or ordered data, each with specific characteristics and examples.
- Record data consists of a fixed set of attributes for each record, while transaction data involves a set of items purchased during a single transaction.
- Graph data can represent molecular structures or webpages, while ordered data includes spatial, temporal, and sequential data, as well as genetic sequence data.
- Data quality issues such as noise, outliers, wrong data, fake data, and missing values can negatively impact data processing efforts.
- Noise can refer to extraneous objects or modification of original values, while outliers are data objects considerably different from most others in the dataset.
- Examples of noise and outliers include distortion of a person's voice on a poor phone line and credit card fraud for noise, and intrusion detection for outliers.
- Reasons for missing values include information not being collected or attributes not being applicable to all cases.
- Data mining examples, such as building a classification model for detecting loan risks using poor data, can lead to credit-worthy candidates being denied loans and more loans being given to individuals that default.
- Detecting and addressing data quality problems, such as noise, outliers, wrong data, fake data, and missing values, is crucial for ensuring accurate data analysis and decision-making.
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Description
Test your knowledge of data types and quality issues with this quiz. Learn about S.S. Stevens' attribute types, discrete and continuous attributes, different data representations, and common data quality issues such as noise, outliers, and missing values. Understand the importance of addressing data quality problems for accurate data analysis and decision-making.
