Data Dimensionality Reduction Techniques Quiz
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Questions and Answers

What is an attribute in the context of data mining?

  • A measurement of length
  • A record or point in a dataset
  • A collection of data objects
  • A property or characteristic of an object (correct)

    • Which term is synonymous with 'attribute' in data mining?

  • Variable (correct)
  • Entity
  • Record
  • Sample

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

  • Records or points in a dataset
  • Numbers or symbols assigned to an attribute (correct)
  • Measurements of length
  • Collections of data objects

    • How are attributes and attribute values distinguished?

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

    What is an example of an attribute value distinction provided in the text?

    <p>Height measured in feet or meters</p> Signup and view all the answers

    In the context of data mining, what does 'ID' refer to?

    <p>A unique identifier for an object</p> Signup and view all the answers

    What is the distinction between 'ID' and 'age' in terms of attribute values?

    <p>ID has no limit but age has a maximum and minimum value</p> Signup and view all the answers

    What is the purpose of measuring an attribute in data mining?

    <p>To describe the properties of objects</p> Signup and view all the answers

    Which type of attribute provides enough information to order objects?

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

    What does a ratio attribute capture?

    <p>All 4 properties</p> Signup and view all the answers

    What type of attribute is temperature in Celsius or Fahrenheit?

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

    What type of attribute is eye color?

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

    What type of attribute is temperature in Kelvin?

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

    Which attribute type has real numbers as attribute values?

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

    What type of attribute is ID numbers?

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

    What type of attribute is calendar dates?

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

    What type of attribute is monetary quantities?

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

    Which attribute type captures only distinctness?

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

    What type of attribute is counts and age?

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

    What type of attribute is height in {tall, medium, short}?

    <p>Ordinal</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 for the similarity measure?

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

    What is the formula for Euclidean Distance?

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

    What is the generalization of Euclidean Distance?

    <p>Minkowski Distance</p> Signup and view all the answers

    What is the parameter 'r' for the Minkowski Distance when it represents the 'supremum' distance?

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

    What is the range for dissimilarity measure?

    <p>(-∞, ∞)</p> Signup and view all the answers

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

    <p>Similarity values of 1, 0.5, 0.09, 0.01, respectively</p> Signup and view all the answers

    What is the measure of plant growth used by ecosystem scientists?

    <p>Net Primary Production (NPP)</p> Signup and view all the answers

    What does proximity refer to?

    <p>Both similarity and dissimilarity measures</p> Signup and view all the answers

    What is the minimum dissimilarity value?

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

    What is the upper limit for dissimilarity measure?

    <p>There is no upper limit</p> Signup and view all the answers

    When is standardization necessary for Euclidean Distance?

    <p>When scales differ</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 will work almost as well as using the entire data sets, if the sample is representative</p> Signup and view all the answers

    What is the main reason for employing sampling in data mining?

    <p>Processing the entire set of data of interest is too expensive or time consuming</p> Signup and view all the answers

    What is the purpose of dimensionality reduction?

    <p>Avoid curse of dimensionality and reduce time and memory required by data mining algorithms</p> Signup and view all the answers

    What technique is used for dimensionality reduction?

    <p>Principal Component Analysis (PCA)</p> Signup and view all the answers

    What does PCA aim to find?

    <p>A projection that captures the largest amount of variation in data</p> Signup and view all the answers

    What is the purpose of data cleaning?

    <p>Dealing with duplicate data issues</p> Signup and view all the answers

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

    <p>Data set may include data objects that are duplicates or almost duplicates of one another</p> Signup and view all the answers

    What is the purpose of feature subset selection?

    <p>To reduce the number of attributes or objects</p> Signup and view all the answers

    What is the aim of discretization and binarization?

    <p>To transform continuous attributes into discrete or binary values</p> Signup and view all the answers

    What is the purpose of attribute transformation?

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

    What does the curse of dimensionality refer to?

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

    What does feature subset selection aim to achieve?

    <p>Remove redundant or irrelevant attributes to reduce data dimensionality</p> Signup and view all the answers

    What is involved in feature creation?

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

    How can data be mapped to a new space?

    <p>Through techniques like Fourier and wavelet transforms</p> Signup and view all the answers

    What does discretization involve?

    <p>Converting a continuous attribute into an ordinal attribute, commonly used in classification</p> Signup and view all the answers

    What does binarization involve?

    <p>Mapping a continuous or categorical attribute into one or more binary variables, commonly used for association analysis</p> Signup and view all the answers

    What is attribute transformation?

    <p>Involves mapping the entire set of attribute values to a new set using functions like xk, log(x), ex, |x|, standardization, and normalization</p> Signup and view all the answers

    What is normalization?

    <p>An attribute transformation technique that adjusts attributes for differences in frequency of occurrence, mean, variance, and range</p> Signup and view all the answers

    What does the Iris Plant data set contain?

    <p>Three flower types and four non-class attributes</p> Signup and view all the answers

    How can discretization be illustrated using the Iris data set?

    <p>Different petal width and length values imply different flower types</p> Signup and view all the answers

    How can discretization be done?

    <p>Using unsupervised or supervised approaches, finding breaks in the data values with or without using class labels</p> Signup and view all the answers

    What are discretization approaches provided as visual examples in the text?

    <p>Equal interval width, equal frequency, and k-means approaches</p> Signup and view all the answers

    What type of data involves records with sets of items, like products purchased at a store?

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

    Which type of data is represented as term vectors with the frequency of terms in the document?

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

    What are some important characteristics of data mentioned in the text?

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

    What type of data involves sequences of transactions, genomic sequence data, and spatio-temporal data?

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

    What is the term for the modification of original values in data?

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

    Which type of data quality problem refers to data objects with significantly different characteristics?

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

    What type of data quality problem can be due to non-collection or inapplicability?

    <p>Missing values</p> Signup and view all the answers

    What does data matrix represent data objects as?

    <p>Points in multi-dimensional space</p> Signup and view all the answers

    Which type of data involves generic graphs, molecules, and webpages?

    <p>Graph-based data</p> Signup and view all the answers

    What can poor data quality negatively impact?

    <p>Data processing efforts and revenue</p> Signup and view all the answers

    What type of data sets include ordered data, transaction data, and graph-based data?

    <p>Graph-based data</p> Signup and view all the answers

    What are some characteristics of data mentioned in the text?

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

    What type of data involves records with sets of items, like products purchased at a store?

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

    What does noise refer to in the context of data quality problems?

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

    What is represented as term vectors with the frequency of terms in the document?

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

    What type of data involves sequences of transactions, genomic sequence data, and spatio-temporal data?

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

    What are some important characteristics of data mentioned in the text?

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

    What type of data sets include generic graphs, molecules, and webpages?

    <p>Graph-based data</p> Signup and view all the answers

    What type of attribute is temperature in Kelvin?

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

    What is an example of a data quality problem mentioned in the text?

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

    What does a data matrix represent data objects as?

    <p>Points in multi-dimensional space</p> Signup and view all the answers

    What does sparsity refer to in the context of data?

    <p>Small number of non-zero elements</p> Signup and view all the answers

    What type of attribute is counts and age?

    <p>Ratio attribute</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

    Study Notes

    Data Dimensionality Reduction Techniques

    • Feature subset selection is used to reduce data dimensionality by removing redundant or irrelevant attributes.
    • Feature creation involves creating new attributes to capture important information more efficiently, using methods such as feature extraction, construction, and mapping data to a new space.
    • Mapping data to a new space can be achieved through techniques like Fourier and wavelet transforms.
    • Discretization involves converting a continuous attribute into an ordinal attribute, commonly used in classification.
    • The Iris Plant data set, available from the UCI Machine Learning Repository, contains three flower types and four non-class attributes.
    • Discretization can be illustrated using the Iris data set, where different petal width and length values imply different flower types.
    • Discretization can be done using unsupervised or supervised approaches, finding breaks in the data values with or without using class labels.
    • Binarization maps a continuous or categorical attribute into one or more binary variables, commonly used for association analysis.
    • Attribute transformation involves mapping the entire set of attribute values to a new set, using functions like xk, log(x), ex, |x|, standardization, and normalization.
    • Normalization is an attribute transformation technique that adjusts attributes for differences in frequency of occurrence, mean, variance, and range.
    • The text provides visual examples of discretization approaches, including equal interval width, equal frequency, and k-means approaches.
    • Attribute transformation and discretization techniques are essential for reducing data dimensionality and preparing data for various data mining tasks.

    Data Mining: Types of Data and Data Quality

    • Association analysis uses asymmetric attributes
    • Types of data sets include record data, data matrix, document data, transaction data, graph-based data, and ordered data
    • Important characteristics of data include dimensionality, sparsity, resolution, and size
    • Record data consists of a collection of records with fixed attributes
    • Data matrix represents data objects as points in multi-dimensional space
    • Document data is represented as term vectors with the frequency of terms in the document
    • Transaction data involves records with sets of items, like products purchased at a store
    • Graph data examples include generic graphs, molecules, and webpages
    • Ordered data includes sequences of transactions, genomic sequence data, and spatio-temporal data
    • Poor data quality can negatively impact data processing efforts and lead to significant revenue loss
    • Data quality problems include noise, outliers, and missing values
    • Noise refers to the modification of original values, while outliers are data objects with significantly different characteristics. Missing values can be due to non-collection or inapplicability, and can be handled by eliminating data objects or estimating missing values.

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    Description

    Test your knowledge of data dimensionality reduction techniques with this quiz. Explore feature subset selection, feature creation, mapping data to a new space, discretization, binarization, attribute transformation, and more. See how these techniques are applied to the Iris Plant data set and their significance in data mining tasks.

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