Data Preprocessing in Data Mining
26 Questions
0 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 the unit of measurement for precipitation in the given data?

  • Millimeters
  • Meters
  • Centimeters (correct)
  • Inches

    • What does the standard deviation of average monthly precipitation represent?

  • The variation in average monthly precipitation (correct)
  • The variation in the entire set of data
  • The variation in sampling techniques
  • The variation in average yearly precipitation

    • Why do statisticians often use sampling?

  • Because it provides more accurate results
  • Because it is a faster method
  • Because obtaining the entire set of data is too expensive or time consuming (correct)
  • Because it is a more reliable method

    • What is the key principle for effective sampling?

    <p>Using a sample will work almost as well as using the entire data set, if the sample is representative</p> Signup and view all the answers

    What does a representative sample have?

    <p>Approximately the same properties as the original set of data</p> Signup and view all the answers

    What is the difference between sampling with replacement and sampling without replacement?

    <p>Objects are not removed from the population as they are selected for the sample in sampling with replacement, while objects are removed in sampling without replacement</p> Signup and view all the answers

    What is the purpose of sampling in data mining?

    <p>To reduce the cost and time of processing the data</p> Signup and view all the answers

    What is simple random sampling?

    <p>A method of sampling where each item has an equal probability of being selected</p> Signup and view all the answers

    What is the primary reason for removing redundant features from a dataset?

    <p>To reduce the dimensionality of the data</p> Signup and view all the answers

    What is the purpose of Principal Components Analysis (PCA) in data mining?

    <p>To capture the largest amount of variation in data</p> Signup and view all the answers

    What is the problem that arises when dimensionality increases in data mining?

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

    What is the purpose of feature creation in data mining?

    <p>To capture the important information in a data set more efficiently</p> Signup and view all the answers

    What is the term for the process of finding a new representation of the data that captures the important information?

    <p>Mapping data to a new space</p> Signup and view all the answers

    What is the advantage of using dimensionality reduction techniques in data mining?

    <p>It reduces the amount of time and memory required by data mining algorithms</p> Signup and view all the answers

    What is the purpose of feature subset selection in data mining?

    <p>To eliminate redundant and irrelevant features</p> Signup and view all the answers

    What is the problem with correlations between time series data?

    <p>They are affected by seasonality</p> Signup and view all the answers

    What is the purpose of data exploration in data mining?

    <p>To identify patterns and relationships in the data</p> Signup and view all the answers

    What is the term for the process of selecting a subset of the most relevant features from the original data?

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

    What is the purpose of aggregation in data preprocessing?

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

    What is the difference between the average monthly precipitation and the average yearly precipitation in the example of precipitation data in Australia?

    <p>The average yearly precipitation has less variability</p> Signup and view all the answers

    What is the advantage of aggregating data?

    <p>It makes the data more stable</p> Signup and view all the answers

    What is the formula for similarity in data mining?

    <p>$\sigma_n \ imes \ ext{sum}_{k=1}^{n} \ ext{omega}_k \ ext{delta}_k \ ext{s}_k(x, y)$</p> Signup and view all the answers

    What is the purpose of data reduction in aggregation?

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

    What is an example of aggregation in real-life?

    <p>Cities aggregated into countries</p> Signup and view all the answers

    What is another term for aggregation in data preprocessing?

    <p>Combining attributes</p> Signup and view all the answers

    What is the period of time for the precipitation data in Australia?

    <p>1982 to 1993</p> Signup and view all the answers

    Study Notes

    Data Preprocessing

    • Aggregation: combining two or more attributes (or objects) into a single attribute (or object)
      • Purpose: data reduction, change of scale, and more stable data
    • Sampling: main technique for data reduction, used for preliminary investigation and final data analysis
      • Key principle: using a sample will work almost as well as using the entire data set, if the sample is representative
    • Discretization and Binarization: transforming continuous data into discrete or binary form
    • Attribute Transformation: changing the scale or format of data
    • Dimensionality Reduction: reducing the number of attributes or features in the data
    • Feature subset selection: selecting a subset of the most relevant features
    • Feature creation: creating new attributes that can capture the important information in a data set more efficiently

    Aggregation

    • Purpose: data reduction, change of scale, and more stable data
    • Examples: aggregating cities into regions, states, or countries, days into weeks, months, or years
    • Effect: aggregated data tends to have less variability

    Sampling

    • Types: simple random sampling, sampling without replacement, sampling with replacement
    • Sample size: affects the representativeness of the sample
    • Importance: sampling is used to reduce the amount of data and make data analysis more efficient

    Dimensionality Reduction

    • Purpose: avoid curse of dimensionality, reduce data size, and enable visualization
    • Techniques: Principal Components Analysis (PCA), Singular Value Decomposition, and others
    • Effect: reduces the number of attributes or features in the data

    Principal Components Analysis (PCA)

    • Goal: find a projection that captures the largest amount of variation in data
    • Result: reduces the dimensionality of the data while retaining most of the information

    Feature Subset Selection

    • Purpose: select a subset of the most relevant features
    • Redundant features: duplicate much or all of the information contained in one or more other attributes
    • Irrelevant features: contain no information that is useful for the data mining task
    • Techniques: many techniques developed, especially for classification

    Feature Creation

    • Purpose: create new attributes that can capture the important information in a data set more efficiently
    • Methodologies: feature extraction, feature construction, and mapping data to new space
    • Examples: extracting edges from images, dividing mass by volume to get density, and Fourier and wavelet analysis

    Data Exploration

    • Statistical measurements: mean, median, mode, quantiles, quartile, percentile, frequency
    • Visualization: x-y scatter, histogram, violin, heatmap, networks, hierarchical plots, spatio-temporal, contour, surface vector field plots, and Chernoff Faces
    • Multivariate data: OLAP, DataCubes

    Studying That Suits You

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

    Quiz Team

    Description

    This quiz covers various data preprocessing techniques including aggregation, sampling, discretization, attribute transformation, and dimensionality reduction. Learn about the importance of data preprocessing in data mining.

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser