• importdata function:

  A = importdata('diabetes.dat')
  
  You can see the contents of A by typing:
  A.data
  A.textdata
  

• tblread function:

  [X,varnames,casenames] = tblread('diabetes.dat', ',')
  
  This uploads tabular data onto X. The first line of the file contains the names of the attributes/features, and the first column contains a label or an id for each data instance.
  

• uiimport function:

  uiimport('diabetes_no_attribute_names.dat');
  
  This starts a wizard that creates three matrices:
  data: containing the data instances
  rowheaders: containing the class labels
  textdata: containing all textdata (in this case same as class labels) as the dataset is numeric.
  

• textscan function:

  Read formatted data from text file or string 
  See help or "doc textscan" to learn more about this function.
  Example:
  
  fid = fopen('contact-lenses.csv');
  C = textscan(fid, '%s %s %s %s %s','delimiter', ',');
  
  >> C{1}
  
  ans = 
  
      'young'
      'young'
      'young'
      'young'
      'young'
      'young'
      'young'
      'young'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'pre-presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
      'presbyopic'
  
  >> C{5}
  
  ans = 
  
      'none'
      'soft'
      'none'
      'hard'
      'none'
      'soft'
      'none'
      'hard'
      'none'
      'soft'
      'none'
      'hard'
      'none'
      'soft'
      'none'
      'none'
      'none'
      'none'
      'none'
      'hard'
      'none'
      'soft'
      'none'
      'none'
  
  >> C{1}{3}
  
  ans =
  
  young
  

• load function:

  load - Load data from MAT-file into workspace
  
  for example:
  c = load('diabetes_numeric_only.csv');
  

• Import Data function:

  Use Matlab's "Import Data ..." function available from the main toolbar menu:
  
  File -> Import Data 
  
  and follow the prompts. 
  

Using the DATASET class in Matlab' Statistics Toolbox

load fisheriris

load hospital 

• See Matlab Central http://www.mathworks.com/matlabcentral/fileexchange/21204-matlab-weka-interface
• Search online for more resources

• Matlab's dimensionality reduction documentation

• Notes on Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP), Bayes' Estimation, Parametric Classification, Parametric Regression

• Useful demo of decision trees, linear and quadratic discriminant analysis, and naive Bayes in Matlab.
• Classification and regression trees.
• Description of the classregtree function.

• K-Means Clustering using Matlab
  • diabetes.dat
  • k_means_clustering_example_2D.m
  • k_means_clustering_example_diabetes.m

• Hierarchical Clustering using Matlab
  • diabetes.dat
  • hierarchical_clustering_example_2D.m
  • hierarchical_clustering_example_diabetes.m

• Expectation Maximization (EM)
  • EM Clustering using Matlab and Weka