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Dimensionality Reduction Techniques in Data Mining Quiz
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Dimensionality Reduction Techniques in Data Mining Quiz

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Questions and Answers

What is an attribute in the context of data mining?

  • An entity or instance
  • A record or point
  • A property or characteristic of an object (correct)
  • A collection of data objects

    • Which term is used interchangeably with 'attribute'?

  • Record
  • Sample
  • Variable (correct)
  • Entity

    • What are attribute values in the context of data mining?

  • Entities or instances
  • Collections of attributes
  • Records or points
  • Numbers or symbols assigned to an attribute (correct)

    • What is the distinction between attributes and attribute values?

    <p>Same attribute can be mapped to different attribute values</p> Signup and view all the answers

    What is an object in the context of data mining?

    <p>A collection of attributes describing an object</p> Signup and view all the answers

    What are some examples of objects in data mining?

    <p>Records</p> Signup and view all the answers

    What is the measurement of length in the context of data mining?

    <p>The way an attribute is measured may not match the attribute's properties</p> Signup and view all the answers

    What are attribute values for ID and age in data mining?

    <p>Integers</p> Signup and view all the answers

    Which type of attribute captures only the order properties of length?

    <p>Ordinal attribute</p> Signup and view all the answers

    What type of attribute preserves both order and additivity properties of length?

    <p>Ratio attribute</p> Signup and view all the answers

    Which attribute type provides enough information to distinguish one object from another?

    <p>Nominal attribute</p> Signup and view all the answers

    What is an example of an interval attribute?

    <p>Calendar dates</p> Signup and view all the answers

    Which type of attribute has all four properties of distinctness, order, addition, and multiplication?

    <p>Ratio attribute</p> Signup and view all the answers

    What is a characteristic of a discrete attribute?

    <p>Has only a finite or countably infinite set of values</p> Signup and view all the answers

    What is a distinguishing feature of a continuous attribute?

    <p>Has real numbers as attribute values</p> Signup and view all the answers

    What is an example of a binary attribute?

    <p>True/false values</p> Signup and view all the answers

    Which type of attribute transformation involves any permutation of values?

    <p>Nominal</p> Signup and view all the answers

    What type of attribute transformation requires an order preserving change of values?

    <p>Ordinal</p> Signup and view all the answers

    What type of attribute transformation involves a unit of measurement existing?

    <p>Interval</p> Signup and view all the answers

    Which type of attribute transformation includes both differences and ratios being meaningful?

    <p>Ratio</p> Signup and view all the answers

    What is the purpose of aggregation in data preprocessing?

    <p>Data reduction and change of scale</p> Signup and view all the answers

    What is the key principle for effective sampling?

    <p>Using a sample that is representative of the original data</p> Signup and view all the answers

    Which type of sampling allows the same object to be picked up more than once?

    <p>Sampling with replacement</p> Signup and view all the answers

    What is the purpose of dimensionality reduction in data mining?

    <p>Avoid curse of dimensionality and reduce time/memory requirements</p> Signup and view all the answers

    In PCA, what do the eigenvectors define?

    <p>The new space</p> Signup and view all the answers

    What is the main issue when merging data from heterogeneous sources?

    <p>Duplicate data</p> Signup and view all the answers

    What is the purpose of feature subset selection?

    <p>To choose the most relevant features for analysis</p> Signup and view all the answers

    What does the curse of dimensionality refer to?

    <p>Data becoming increasingly sparse as dimensionality increases</p> Signup and view all the answers

    What is the purpose of attribute transformation in data preprocessing?

    <p>To convert attributes into a more suitable format for analysis</p> Signup and view all the answers

    What is the main technique employed for data selection?

    <p>Sampling</p> Signup and view all the answers

    What does sampling without replacement entail?

    <p>As each item is selected, it is removed from the population</p> Signup and view all the answers

    What is the purpose of discretization and binarization in data preprocessing?

    <p>To convert continuous attributes into categorical or binary ones</p> Signup and view all the answers

    What are some important characteristics of data?

    <p>Dimensionality, sparsity, resolution, and size</p> Signup and view all the answers

    Which type of data involves a set of items in each record?

    <p>Transaction data</p> Signup and view all the answers

    What does noise refer to in data quality problems?

    <p>Modification of original values</p> Signup and view all the answers

    What does record data consist of?

    <p>A collection of records with fixed attributes</p> Signup and view all the answers

    How is document data represented?

    <p>As term vectors with term frequency values</p> Signup and view all the answers

    What are examples of graph data?

    <p>Generic graphs, molecules, and webpages</p> Signup and view all the answers

    What type of data includes sequences of transactions and spatio-temporal data?

    <p>Ordered data</p> Signup and view all the answers

    What negatively affects data processing efforts and can lead to significant costs?

    <p>Poor data quality</p> Signup and view all the answers

    What type of data sets represent data objects as points in a multi-dimensional space?

    <p>Data matrix</p> Signup and view all the answers

    What do outliers refer to in data quality problems?

    <p>Considerably different data objects</p> Signup and view all the answers

    How is data quality problem of missing values usually handled?

    <p>By elimination or estimation</p> Signup and view all the answers

    What type of data involves a collection of records with fixed attributes?

    <p>Record data</p> Signup and view all the answers

    Which technique aims to reduce redundant and irrelevant features in the dataset?

    <p>Feature subset selection</p> Signup and view all the answers

    What does feature creation involve?

    <p>Creating new attributes that capture important information more efficiently</p> Signup and view all the answers

    Which technique involves converting continuous attributes into ordinal attributes, commonly used in classification?

    <p>Discretization</p> Signup and view all the answers

    What does binarization involve?

    <p>Mapping continuous or categorical attributes into one or more binary variables</p> Signup and view all the answers

    What type of attribute transformation adjusts differences among attributes in terms of frequency of occurrence, mean, variance, and range?

    <p>Normalization</p> Signup and view all the answers

    Which data set is used as an example to illustrate discretization?

    <p>Iris Plant data set</p> Signup and view all the answers

    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 $|x|$?

    <p>Attribute transformation</p> Signup and view all the answers

    What does mapping data to a new space involve?

    <p>Achieving through techniques like Fourier transform and wavelet transform</p> Signup and view all the answers

    Which method involves creating new attributes that capture important information more efficiently than the original attributes?

    <p>Feature creation</p> Signup and view all the answers

    What is the goal of attribute transformation?

    <p>To remove unwanted, common signals and adjust for differences among attributes</p> Signup and view all the answers

    What does discretization involve?

    <p>Converting continuous attributes into ordinal attributes</p> Signup and view all the answers

    Which technique maps continuous or categorical attributes into one or more binary variables?

    <p>Binarization</p> Signup and view all the answers

    What does standardization in statistics refer to?

    <p>Subtracting off the means and dividing by the standard deviation</p> Signup and view all the answers

    What is the range in which similarity often falls?

    <p>[0,1]</p> Signup and view all the answers

    What is the formula for Euclidean Distance?

    <p>$dist = \n \bigg( \n \bigg( p_k - q_k \n \bigg)^2 \n \bigg)^{1/2}$</p> Signup and view all the answers

    What is the parameter 'r' in Minkowski Distance?

    <p>Number of dimensions (attributes)</p> Signup and view all the answers

    What is the Minkowski Distance for r = 2?

    <p>Euclidean distance (L2 norm)</p> Signup and view all the answers

    What is the range of dissimilarity often considered?

    <p>[0,∞)</p> Signup and view all the answers

    What is the transformation equation for dissimilarity values of 0, 1, 10, 100?

    <p>Transformation equation results in similarity values of 1.00, 0.99, 0.00, 0.00, respectively</p> Signup and view all the answers

    What is the formula for Minkowski Distance?

    <p>$dist = \n \bigg( \n \bigg( |p_k - q_k| \n \bigg)^r \n \bigg)^{1/r}$</p> Signup and view all the answers

    What is the Minkowski Distance for r = ∞?

    <p>“supremum” (Lmax norm, L∞ norm) distance</p> Signup and view all the answers

    What is the minimum dissimilarity often considered?

    <p>0</p> Signup and view all the answers

    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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    Related Documents

    Week_3_4.pdf

    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.

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