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Questions and Answers
What is an attribute in the context of data mining?
What is an attribute in the context of data mining?
Which term is used interchangeably with 'attribute'?
Which term is used interchangeably with 'attribute'?
What are attribute values in the context of data mining?
What are attribute values in the context of data mining?
What is the distinction between attributes and attribute values?
What is the distinction between attributes and attribute values?
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What is an object in the context of data mining?
What is an object in the context of data mining?
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What are some examples of objects in data mining?
What are some examples of objects in data mining?
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What is the measurement of length in the context of data mining?
What is the measurement of length in the context of data mining?
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What are attribute values for ID and age in data mining?
What are attribute values for ID and age in data mining?
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Which type of attribute captures only the order properties of length?
Which type of attribute captures only the order properties of length?
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What type of attribute preserves both order and additivity properties of length?
What type of attribute preserves both order and additivity properties of length?
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Which attribute type provides enough information to distinguish one object from another?
Which attribute type provides enough information to distinguish one object from another?
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What is an example of an interval attribute?
What is an example of an interval attribute?
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Which type of attribute has all four properties of distinctness, order, addition, and multiplication?
Which type of attribute has all four properties of distinctness, order, addition, and multiplication?
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What is a characteristic of a discrete attribute?
What is a characteristic of a discrete attribute?
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What is a distinguishing feature of a continuous attribute?
What is a distinguishing feature of a continuous attribute?
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What is an example of a binary attribute?
What is an example of a binary attribute?
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Which type of attribute transformation involves any permutation of values?
Which type of attribute transformation involves any permutation of values?
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What type of attribute transformation requires an order preserving change of values?
What type of attribute transformation requires an order preserving change of values?
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What type of attribute transformation involves a unit of measurement existing?
What type of attribute transformation involves a unit of measurement existing?
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Which type of attribute transformation includes both differences and ratios being meaningful?
Which type of attribute transformation includes both differences and ratios being meaningful?
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What is the purpose of aggregation in data preprocessing?
What is the purpose of aggregation in data preprocessing?
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What is the key principle for effective sampling?
What is the key principle for effective sampling?
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Which type of sampling allows the same object to be picked up more than once?
Which type of sampling allows the same object to be picked up more than once?
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What is the purpose of dimensionality reduction in data mining?
What is the purpose of dimensionality reduction in data mining?
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In PCA, what do the eigenvectors define?
In PCA, what do the eigenvectors define?
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What is the main issue when merging data from heterogeneous sources?
What is the main issue when merging data from heterogeneous sources?
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What is the purpose of feature subset selection?
What is the purpose of feature subset selection?
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What does the curse of dimensionality refer to?
What does the curse of dimensionality refer to?
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What is the purpose of attribute transformation in data preprocessing?
What is the purpose of attribute transformation in data preprocessing?
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What is the main technique employed for data selection?
What is the main technique employed for data selection?
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What does sampling without replacement entail?
What does sampling without replacement entail?
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What is the purpose of discretization and binarization in data preprocessing?
What is the purpose of discretization and binarization in data preprocessing?
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What are some important characteristics of data?
What are some important characteristics of data?
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Which type of data involves a set of items in each record?
Which type of data involves a set of items in each record?
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What does noise refer to in data quality problems?
What does noise refer to in data quality problems?
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What does record data consist of?
What does record data consist of?
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How is document data represented?
How is document data represented?
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What are examples of graph data?
What are examples of graph data?
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What type of data includes sequences of transactions and spatio-temporal data?
What type of data includes sequences of transactions and spatio-temporal data?
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What negatively affects data processing efforts and can lead to significant costs?
What negatively affects data processing efforts and can lead to significant costs?
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What type of data sets represent data objects as points in a multi-dimensional space?
What type of data sets represent data objects as points in a multi-dimensional space?
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What do outliers refer to in data quality problems?
What do outliers refer to in data quality problems?
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How is data quality problem of missing values usually handled?
How is data quality problem of missing values usually handled?
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What type of data involves a collection of records with fixed attributes?
What type of data involves a collection of records with fixed attributes?
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Which technique aims to reduce redundant and irrelevant features in the dataset?
Which technique aims to reduce redundant and irrelevant features in the dataset?
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What does feature creation involve?
What does feature creation involve?
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Which technique involves converting continuous attributes into ordinal attributes, commonly used in classification?
Which technique involves converting continuous attributes into ordinal attributes, commonly used in classification?
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What does binarization involve?
What does binarization involve?
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What type of attribute transformation adjusts differences among attributes in terms of frequency of occurrence, mean, variance, and range?
What type of attribute transformation adjusts differences among attributes in terms of frequency of occurrence, mean, variance, and range?
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Which data set is used as an example to illustrate discretization?
Which data set is used as an example to illustrate discretization?
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What method involves mapping the entire set of values of an attribute to a new set of replacement values using functions such as $x^k$, $ ext{log}(x)$, $e^x$, and $|x|$?
What method involves mapping the entire set of values of an attribute to a new set of replacement values using functions such as $x^k$, $ ext{log}(x)$, $e^x$, and $|x|$?
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What does mapping data to a new space involve?
What does mapping data to a new space involve?
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Which method involves creating new attributes that capture important information more efficiently than the original attributes?
Which method involves creating new attributes that capture important information more efficiently than the original attributes?
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What is the goal of attribute transformation?
What is the goal of attribute transformation?
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What does discretization involve?
What does discretization involve?
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Which technique maps continuous or categorical attributes into one or more binary variables?
Which technique maps continuous or categorical attributes into one or more binary variables?
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What does standardization in statistics refer to?
What does standardization in statistics refer to?
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What is the range in which similarity often falls?
What is the range in which similarity often falls?
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What is the formula for Euclidean Distance?
What is the formula for Euclidean Distance?
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What is the parameter 'r' in Minkowski Distance?
What is the parameter 'r' in Minkowski Distance?
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What is the Minkowski Distance for r = 2?
What is the Minkowski Distance for r = 2?
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What is the range of dissimilarity often considered?
What is the range of dissimilarity often considered?
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What is the transformation equation for dissimilarity values of 0, 1, 10, 100?
What is the transformation equation for dissimilarity values of 0, 1, 10, 100?
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What is the formula for Minkowski Distance?
What is the formula for Minkowski Distance?
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What is the Minkowski Distance for r = ∞?
What is the Minkowski Distance for r = ∞?
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What is the minimum dissimilarity often considered?
What is the minimum dissimilarity often considered?
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Study Notes
Introduction to Data Mining: Dimensionality Reduction Techniques
- Dimensionality reduction includes techniques such as feature subset selection, feature creation, and attribute transformation.
- Feature subset selection aims to reduce redundant and irrelevant features in the dataset.
- Feature creation involves creating new attributes that capture important information more efficiently than the original attributes.
- Mapping data to a new space can be achieved through techniques like Fourier transform and wavelet transform.
- Discretization involves converting continuous attributes into ordinal attributes, commonly used in classification.
- The Iris Plant data set, obtained from the UCI Machine Learning Repository, is used as an example to illustrate discretization.
- Discretization methods include unsupervised and supervised approaches, as well as equal interval width, equal frequency, and K-means approaches.
- Binarization maps continuous or categorical attributes into one or more binary variables, often used for association analysis.
- Attribute transformation involves mapping the entire set of values of an attribute to a new set of replacement values using functions such as xk, log(x), ex, and |x|.
- Normalization is a type of attribute transformation that adjusts differences among attributes in terms of frequency of occurrence, mean, variance, and range.
- The goal of attribute transformation is to remove unwanted, common signals and adjust for differences among attributes.
- These dimensionality reduction techniques are crucial for improving the efficiency and effectiveness of data mining tasks.
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Description
Test your knowledge of dimensionality reduction techniques in data mining with this quiz. Explore feature subset selection, feature creation, attribute transformation, discretization, binarization, and normalization methods. Learn about their applications and the Iris Plant data set example. Mastering these techniques is essential for enhancing the efficiency and effectiveness of data mining tasks.