Correlation Matrix will help you understand the corelation between various variables. It is a Symmetrical Matrix where ij element in the matrix is equal to the correlation coefficient between the variable i and j. The diagonal element are always equivalent to 1. (Thanks http://www.statistics.com/glossary&term_id=310).
Purpose of using Correlation Matrix:
 To identify the outliers
 To identify the colinearity exists between the variables.
 Used for regression analysis
Simple understanding:
Correlation is a number between +1 and 1 that helps you to measure the relationship between two variables which are being linear(e.g., Higher the income, Higher the Tax) where correlation is +1 or positive, on the other hand (e.g., every item sold will reduce your inventory) where the correlation is 1 or Negative. If it’s near to zero it means that corelation doesn’t exists (e.g., Average temperature in summer, Average sales of news magazines) which would reflect linear independence between variables. Also its very important to understand the correlation would not affect by the scale of the variables and how its measured.
About the Dataset:
Source: Thanks to Fuel Consumption Ratings from data.gc.ca , Link: http://data.gc.ca/data/en/dataset/98f1a129f6284ce4b24d6f16bf24dd64
The dataset is which I have used is a refined one of the data from the above link. The dataset I use in this post has the following attributes:
 Make – Car Make
 Class – Referred as given below
COMPACT

C

SPECIAL PURPOSE

SP

MIDSIZE

M

SUBCOMPACT

S

TWOSEATER

T

STATION WAGON

W

FULLSIZE

L

PICKUP TRUCK

PU

LARGE VAN

F

MINIVAN

V

 Engine
 Transmission
 Fuel Type
 City (Fuel Consumption during City drive in mi/gallons)
 Hwy (Fuel Consumption during Highway drive in mi/gallons)
Tool Usage:
In this post we will use Rapid Miner tool to understand the Fuel Consumption of cars in Canada for the Year 2013 data related variables.
Steps to evaluate correlation Matrix:
Step 1: Open Rapid Miner which you can download from rapidminer.com
Step 2: Import the data from the local drive. In my case I have kept it in excel format, for that you have to click “Import Excel Sheet…” under the Repository Tab. Also you can look at a repository named “SivaRepository” which I have created previously.
Step 3: After you import and click Finish you will something like this as given below, also you can see the log to identify if there are any errors, I have given the name for the dataset as CanadaCarsFuelConsumption2013.
Step 4: Now we will do the correlation matrix from this data. Select the correlation matrix operator from the Operators under Modeling/Correlation and Dependency Computation Section.
Step 5: Now also drag the CanadaCarsFuelConsumption2013 dataset to the process area and connect the out to the exa of the CorrelationMatrix Operator. Then connect the mat output to the process res for the output.
Step 6: Now let’s run the process to the see the results.
Result:
Based on this outcome we can realize that City, Hwy and Fuel related variables have a close correlation that other parameters as the relationship is very positive when compared to other variables. We can also look at the pair wise tables to have better understanding.