Data Dimensionality Reduction Techniques Quiz
79 Questions
2 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

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.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Week_3_4.pdf

    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.

    More Like This

    Dimensionality Reduction Techniques in Data Mining Quiz
    56 questions

    Dimensionality Reduction Techniques in Data Mining Quiz

    WinningTropicalRainforest avatar
    WinningTropicalRainforest
    Data Mining
    95 questions

    Data Mining

    WinningTropicalRainforest avatar
    WinningTropicalRainforest
    Data Mining Concepts Quiz
    207 questions

    Data Mining Concepts Quiz

    WinningTropicalRainforest avatar
    WinningTropicalRainforest
    Data Pre-Processing III: Data Reduction
    21 questions

    Data Pre-Processing III: Data Reduction

    HeartwarmingFluxus avatar
    HeartwarmingFluxus
    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser