Data Preprocessing Basics
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Data Preprocessing Basics

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Questions and Answers

Which of the following best describes 'noisy data'?

  • Data that is incomplete due to missing values.
  • Data that contains errors or outliers. (correct)
  • Data that is transformed for better analysis.
  • Data that lacks certain attributes of interest.

    • What is one method for handling missing data effectively?

  • Fill it in with a random value unrelated to the dataset.
  • Fill it in with the attribute mean/median/mode for the same class. (correct)
  • Ignore the tuple regardless of its attributes.
  • Delete all records containing any missing values.

    • Which data cleaning issue does not relate to data integration?

  • Duplicate records causing discrepancies.
  • Incorrect values or outliers in numerical data. (correct)
  • Inconsistent codes or naming conventions.
  • Missing attribute values in records.

    • In the context of data handling, what is binning?

    <p>Partitioning data into bins for smoothing techniques.</p> Signup and view all the answers

    When might ignoring a tuple be an appropriate action for handling missing data?

    <p>When the class label is missing during classification.</p> Signup and view all the answers

    What is the purpose of the first principal component (PC) in PCA?

    <p>To capture the direction of maximum variance from the origin</p> Signup and view all the answers

    What is the first step in the PCA algorithm?

    <p>Normalize the data matrix</p> Signup and view all the answers

    Which method is NOT a technique for attribute subset selection?

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

    Which statement best describes the role of eigenvalues in PCA?

    <p>They determine the amount of variance explained by each principal component</p> Signup and view all the answers

    What is the main purpose of removing redundant attributes during attribute subset selection?

    <p>To reduce computational complexity and improve model performance</p> Signup and view all the answers

    What is the primary role of data integration in data management?

    <p>To combine data from multiple sources into a coherent store.</p> Signup and view all the answers

    What is meant by 'dimensionality reduction' in data analysis?

    <p>Reducing the number of features in a dataset to improve analysis.</p> Signup and view all the answers

    Which of the following is a consequence of the 'curse of dimensionality'?

    <p>Greater sparsity of data and less meaningful distances.</p> Signup and view all the answers

    What technique is commonly used for dimensionality reduction?

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

    In data integration, how can redundancy be handled effectively?

    <p>By conducting correlation analysis to detect redundant attributes.</p> Signup and view all the answers

    Why is achieving a reduced representation of a dataset important?

    <p>To improve the speed and performance of data analysis.</p> Signup and view all the answers

    What can be affected negatively by high dimensionality in a dataset?

    <p>The significance of correlation between points.</p> Signup and view all the answers

    What is one of the main reasons for applying data reduction strategies?

    <p>To achieve similar analytical results with a smaller data volume.</p> Signup and view all the answers

    What is the primary purpose of discretization in data preparation?

    <p>To divide continuous attributes into intervals</p> Signup and view all the answers

    In Z-score normalization, what does the formula v' = (v - μA) / σA represent?

    <p>μA is the sample mean of the dataset</p> Signup and view all the answers

    What is a characteristic of normalization by decimal scaling?

    <p>It uses the smallest integer j such that Max(|ν’|) &lt; 1</p> Signup and view all the answers

    Which data quality factor pertains to whether the data is suitable for a given purpose?

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

    What type of discretization combines two or more intervals into a broader interval?

    <p>Bottom-up Merging</p> Signup and view all the answers

    What is the main purpose of data transformation in data preprocessing?

    <p>To map the entire set of values of a given attribute to a new set of replacement values</p> Signup and view all the answers

    Which method would be classified as a non-parametric method of data reduction?

    <p>Random sampling</p> Signup and view all the answers

    What does min-max normalization achieve?

    <p>Maps data values to a specified range</p> Signup and view all the answers

    In data preprocessing, what is the main goal of data cleaning?

    <p>To ensure data is accurately and consistently formatted</p> Signup and view all the answers

    Which of the following statements correctly describes L1-regularization?

    <p>It encourages the sparsity of the feature coefficients</p> Signup and view all the answers

    Which technique specifically focuses on reducing data volume through alternatives to original representations?

    <p>Numerosity reduction</p> Signup and view all the answers

    What is the key feature of parametric methods for data reduction?

    <p>They rely on a specific distribution to well describe the data</p> Signup and view all the answers

    In the context of data discretization, what is a primary goal?

    <p>To classify continuous data into distinct categories</p> Signup and view all the answers

    Study Notes

    Data Preprocessing

    • Data Cleaning is the process of identifying and correcting inaccurate, incomplete, noisy, or inconsistent data in a dataset.
    • Real-world data is often "dirty" due to factors like faulty instruments, human or computer errors, or transmission errors.
    • Incomplete Data lacks attribute values, attributes of interest, or may contain only aggregate data. For example, "Occupation=''" represents missing data.
    • Noisy Data contains errors or outliers. For example, "Salary='-10'" indicates an error.
    • Inconsistent Data has discrepancies in codes or names. For example, "Age='42'" and "Birthday='03/07/2010'" are inconsistent or "Rating" values changed from "1, 2, 3" to "A, B, C"
    • Handling Missing Data:
      • Ignore the tuple: This strategy works well when the class label is missing in classification tasks, but is less effective when missing value percentages vary significantly across attributes.
      • Fill with a global constant: Use a value like "unknown," but this may create a new class.
      • Use the attribute mean/median/mode: Fill in the missing data with these measures.
      • Use the attribute mean/median/mode for all samples belonging to the same class: This method employs data specific to the class.
      • Use the most probable value: Employ inference methods like Bayesian formulas or decision trees for estimation.
    • Handling Noisy Data:
      • Binning: Organize data into sorted bins. Smooth values by using bin means, medians, or boundaries.
      • Regression: Fit the data to regression functions to smooth outliers.
      • Clustering: Identify and remove outliers.
    • Data Integration combines data from various sources into a unified data store.
    • Schema Integration involves integrating metadata from different sources, for example, aligning "cust-id" and "cust-#" between tables.
    • Handling Redundancy in Data Integration:
      • Object Identification: Addresses situations where the same attribute or object has different names in various databases.
      • Derivable Data: Recognizes where one attribute is a derived attribute in another table.
      • Redundant Attributes can be detected through correlation analysis.
    • Data Reduction aims to simplify the data representation while minimizing volume but maintaining analytical results.
      • Data reduction becomes crucial when databases or data warehouses store massive data volumes.
      • Data Reduction Techniques Include:
        • Dimensionality Reduction: Reduces the number of input features.
        • Numerosity Reduction: Reduces the number of data points.

    Data Reduction 1: Dimensionality Reduction

    • Curse of Dimensionality: As the number of dimensions (features) increases, data becomes sparser.
      • Density and distance between data points become less meaningful.
      • The number of possible subspaces grows exponentially, making analysis difficult.
    • Dimensionality reduction techniques:
      • Principal Component Analysis (PCA): A linear transformation method that projects data onto a lower-dimensional subspace while retaining as much variance as possible.
        -Supervised and nonlinear techniques: Include feature selection methods.

    Data Reduction 2: Numerosity Reduction

    • Numerosity Reduction: Techniques for deriving smaller, alternative data representations than the raw data.
    • Non-parametric methods:
      • Random sampling: Draws a subset of the data randomly.
      • Stratified sampling: Randomly samples based on strata in the data (e.g., gender, age groups).
      • Histograms: Group the data into bins and count the occurrences within each bin.
      • Clustering: Groups similar data points together.
    • Parametric methods:
      • Regression: Finds mathematical relationships between variables to estimate data.

    Data Transformation

    • Data Transformation involves changing the representation of data without losing information. This can be done with methods like normalization or discretization.
    • Normalization: Rescales data to fit within a smaller specified range.
      • Min-max normalization: Maps data to a new range between a minimum and maximum value.
      • Z-score normalization: Scales data by subtracting the mean and dividing by the standard deviation.
      • Normalization by decimal scaling: Divides values by the highest power of 10 such that the maximum absolute value is less than 1.
    • Discretization: Groups continuous data into intervals.
      • Discretization simplifies analysis by transforming continuous values into discrete categories.
      • Discretization can be performed recursively on an attribute.
      • Split (top-down) and merge (bottom-up) approaches are common methods.

    Summary of Data Preparation

    • Good data quality is essential: Accuracy, completeness, consistency, timeliness, believability, and interpretability are key attributes.
    • Data cleaning addresses missing values, noisy data, and outliers.
    • Data integration tackles entity identification, redundancy removal, and inconsistency detection.
    • Data reduction techniques like dimensionality reduction and numerosity reduction reduce data volume and complexity.
    • Data transformation and discretization help prepare data for analysis.

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    Related Documents

    Week2A (2).pdf

    Description

    This quiz covers the essential concepts of data preprocessing, specifically focusing on data cleaning techniques. It includes identifying and managing issues like incomplete, noisy, and inconsistent data. Test your understanding of how to handle missing values and improve data quality.

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