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Data Preprocessing

Data Preprocessing​

  1. Aggregation
  2. Attribute Transformation
  3. Dimensionality Reduction
  • Feature creation
  • Feature subset selection
  1. Discretization and Binarization
  2. Sampling

Aggregation​

  • Combining two or more attributes (or objects) into a single attribute (or object)
  • Purpose
    • Data reduction
      • Reduce the number of attributes or objects
    • Change of scale
      • Cities aggregated into regions, states, countries, etc
    • More stable data
      • Aggregated data tends to have less variability

Discretization​

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Attribute Transformation​

  • A function that maps the entire set of values of a given attribute to a new set of replacement values such that each old value can be identified with one of the new values
    • Simple functions: x^k^, log(x), e^x^, |x|
    • Standardization and Normalization

Similarity and Dissimilarity​

  • Similarity
    • Numerical measure of how alike two data objects are
    • Is higher when objects are more alike
    • Often falls in the range [0,1]
  • Dissimilarity
    • Numerical measure of how different are two data objects
    • Lower when objects are more alike
    • Minimum dissimilarity is often 0
    • Upper limit varies
  • Proximity refers to a similarity or dissimilarity

Similarity/Dissimilarity for Simple Attributes​

p and q are the attribute values for two data objects

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Types​

  1. Euclidean Distance
  2. Mahalanobis Distance
  3. Manhattan Distance
  4. Jaccard Similarity
  5. Minkowski Distance
  6. Cosine Similarity

Euclidean Distance​

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  • Where n is the number of dimensions (attributes) and pk and qk are, respectively, the k^th^ attributes (components) or data objects p and q.
  • Standardization is necessary, if scales differ

Mahalanobis Distance​

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  • For red points, the Euclidean distance is 14.7, Mahalanobis distance is 6

Cosine Similarity​

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Cosine Similarity - GeeksforGeeks

Cosine similarity: How does it measure the similarity, Maths behind and usage in Python | by Varun | Towards Data Science

Similarity Between Binary Vectors​

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Correlation​

  • Correlation measures the linear relationship between objects
  • To compute correlation, we standardize data objects, p and q, and then take their dot product

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Visually Evaluating Correlation​

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  • Scatter plots showing the similarity from -1 to 1

Tidy Data​

https://vita.had.co.nz/papers/tidy-data.pdf

https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html