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Published

Data in Data mining

Data in Data mining

Published in Technology
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Transcript

  • 1. DATA FOR DATA MINING
  • 2. Types of variables
    Can be divided into two main types:
    Categorical attributes - Nominal, binary and ordinal variables
    Continuous attributes– integer, interval-scaled and ratio-scaled variables
    Ignore attribute (optional) - Variables which are of no significance
  • 3. Data Cleaning
    Erroneous values can be divided into:
    Noisy value: Valid for the dataset, but incorrectly recorded
    Invalid values: Can be easily detected and removed/corrected
    Noise detection:
    Peaks in the dataset
    Some values outside the normal range: Such values could even be genuine (called as Outliers)
  • 4. Missing Values
    Reasons of occurrence:
    Equipment malfunction
    Additional fields were added later
    Non-availability of information
    Strategies to deal with missing values
    Discard instances
    Replace by most frequent/average value
  • 5. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at www.dataminingtools.net