Data Dissimilarity and Normalization Concepts

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Questions and Answers

Which measurement should be used specifically for numeric attributes to describe central tendency?

  • Median
  • Range
  • Mean (correct)
  • Mode

In which situation would you use the Jaccard coefficient for dissimilarity measurement?

  • When assessing similarity between nominal attributes
  • When comparing objects with ordinal attributes
  • When examining binary attributes (correct)
  • When analyzing numeric attributes

What is the primary function of a correlation matrix?

  • To normalize numeric data
  • To display the central value of attributes
  • To measure dissimilarity between objects
  • To identify redundancy and correlation among variables (correct)

Which type of attributes can be described with symmetric/asymmetric binary dissimilarity?

<p>Binary attributes (C)</p> Signup and view all the answers

What is the primary goal of schema integration in the entity identification problem?

<p>To avoid errors during data transformation (A)</p> Signup and view all the answers

What is the main goal of attribute subset selection in data reduction strategies?

<p>To find a minimal set of attributes that preserves the original probability distribution (D)</p> Signup and view all the answers

Which of the following sampling methods allows tuples to be drawn more than once?

<p>Simple random sample with replacement (SRSWR) (A)</p> Signup and view all the answers

What is the primary objective of regression in data analysis?

<p>To create a linear model that estimates relationships between variables (A)</p> Signup and view all the answers

Which dimensionality reduction technique involves transforming data to a compressed representation?

<p>Wavelet transforms (B)</p> Signup and view all the answers

What type of sampling generates a sample by dividing tuples into groups and selecting complete groups randomly?

<p>Cluster sample (D)</p> Signup and view all the answers

Flashcards

Numeric Data Normalization

A process that ensures all numerical attributes in a dataset have equal weight during similarity calculations.

Minkowski Distance

A method to measure similarity/dissimilarity between objects with numerical attributes.

Attribute Types

Categorize attributes as nominal, binary, ordinal, or numerical, guiding similarity/dissimilarity calculations.

Nominal Attributes

Attributes describing categories or labels, like eye color.

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Binary Attributes

Attributes indicating the presence or absence of a characteristic.

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Central Tendency

Measures of the "center" of a dataset's numerical values (e.g., mean, median, mode).

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Metadata

Detailed information about data, including type, range, null handling.

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Entity Identification Problem

Matching corresponding objects across different data sources or databases.

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Correlation Matrix

A table showing correlation coefficients between variables.

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Data Reduction

Techniques for representing a large dataset with a smaller, yet similar, representation.

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Dimensionality Reduction

Reducing the number of attributes (features) in a dataset.

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Numerosity Reduction

Replacing original data with a smaller representative data set.

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Parametric Method

Data reduction that estimates data using parameters only (e.g., mean, variance).

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Nonparametric Method

Data reduction that stores the representative dataset directly.

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Attribute Subset Selection

Choosing a minimal set of attributes to accurately represent the entire data set.

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Attribute Construction

Creating new attributes from existing ones.

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Regression

Data modeling to fit a straight line (relationship).

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Regression Line Equation

y = wx + b, where 'w' and 'b' are coefficients determined by minimizing errors.

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Sampling

Representing a large dataset by a smaller random sample.

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SRSWOR

Simple Random Sampling without Replacement.

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SRSWR

Simple Random Sampling with Replacement.

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Cluster Sample

Random sampling of clusters of data to reduce data.

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Stratified Sample

Data is divided into groups (strata), and a sample is selected from each group.

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Study Notes

Data Dissimilarity and Normalization

  • Numeric data dissimilarity requires normalization to equalize attribute weights.
  • Example: Height (meters) and weight (grams) attributes. One will likely dominate due to different scales.

Measuring Data Similarity/Dissimilarity

  • Attributes can be nominal, binary, ordinal, or numeric.
  • Use mean, SD, median, and mode to measure numeric attribute central tendency.
  • Use median and mode for other attributes.
  • Use:
    • Matching percent for nominal attributes.
    • Symmetric/asymmetric binary dissimilarity or Jaccard coefficient for binary attributes.
    • Minkowski distance for numeric attributes.

Entity Identification Problem

  • Schema integration and object matching.
  • Metadata (name, meaning, data type, permissible range, and null rules) helps avoid integration and data transformation errors.

Data Reduction Strategies

  • Dimensionality Reduction: Reduce the number of attributes; methods include wavelet transforms and Principal Component Analysis (PCA). Also attribute subset selection.
  • Numerosity Reduction: Replace original data with smaller data representation.
    • Parametric: Estimate data using only parameters.
    • Nonparametric: Store reduced representations of the data.
    • Compression: Transformations for "compressed" representation.
  • Attribute SubsetSelection: Determine a minimal set of attributes to maintain the original data distribution.
  • Attribute Construction: Create new attributes, e.g., area from height and width.

Regression

  • Modeling data for fitting a straight line.
  • Regression line equation: y = wx + b (w and b are regression coefficients).
  • Calculated using least squares method (minimizing error between actual and estimated lines)

Sampling

  • Represent a large dataset using a smaller, random sample.
  • Simple Random Sample (SRS):
    • Without replacement (SRSWOR): Draw 's' of 'N' tuples ('s' < 'N').
    • With replacement (SRSWR): Similar to SRSWOR but each drawn tuple is recorded, then replaced for possible reuse.
  • Cluster Sampling: Tuples are grouped into clusters, and an SRS of clusters is obtained.
  • Stratified Sampling: Tuples are divided into strata and SRS is generated within each stratum.

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