Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js

EDA - Visualisation

Description

Bsc Hons Data Mining Mind Map on EDA - Visualisation, created by Steve Hiscock on 15/12/2013.
Steve Hiscock
Mind Map by Steve Hiscock, updated more than 1 year ago
Steve Hiscock
Created by Steve Hiscock over 11 years ago
26
0
1 2 3 4 5 (0)

Resource summary

EDA - Visualisation
  1. Basic Plots
    1. When combining categories in histograms
      1. 1. Combine the 2> values
        1. 2. Divide the values by the number of bars being represented for that range
          1. 3. This will create an averaged set of bars across the range (Changing the shape of the histogram)
        2. Bars don't have to be the same width - Its all about AREA.... Not HEIGHT
        3. Distribution Shapes

          Attachments:

          1. Bulk of Density
            1. Modal Values
              1. irregular
              2. Relative Frequency Density - Asymmetrical
                1. Outliers
                  1. Measures of location
                    1. Mean
                      1. Add all the values and divide by the number of values
                      2. Mode
                        1. Most common value
                          1. Modal Class vs. Mode Tables. Can appear differently. Class is important. The resolution/number of bars can better determine the mode
                          2. Median
                            1. The middle number
                            2. Variance = average of squared differences about the mean
                              1. Always divide by one less than the sample size - Don't include your mean
                                1. Measures the spread of the data
                                  1. Variance Square Rooted = Standard Deviation
                                    1. How spread out the data is
                                      1. Zero = Max & Min's the same
                                  2. Skew - 0 = Symeteric
                                    1. Kurtosis
                                      1. Tail Fatness - Higher numbers thinner tails
                                    Show full summary Hide full summary

                                    0 comments

                                    There are no comments, be the first and leave one below:

                                    Similar

                                    Data Warehousing and Mining
                                    i7752068
                                    Insurance Policy Advisor
                                    Sufiah Takeisu
                                    Data Mining Part 1
                                    Kim Graff
                                    Minería de Datos.
                                    Marcos Soledispa
                                    Machine Learning
                                    Alberto Ochoa
                                    Data Mining from Big Data 4V-s
                                    Prohor Leykin
                                    Model Roles
                                    Steve Hiscock
                                    Data Mining Process
                                    Steve Hiscock
                                    Data Mining Tasks
                                    Steve Hiscock
                                    Distribution Types
                                    Steve Hiscock