n Module 4, you used Weka to mine some association rules.  In this Critical Thinking assignment you will use Weka to cluster some data.  Your assignment is to cluster data points using K-Means clustering algorithm. 

Here is a step-by-step guide to loading the file and clustering the data.

1. Save the “credit-g.arff” file to someplace on your computer that you can quickly find it. This could be a folder in documents or on your desktop.
2. Open Weka and go to the Explorer. 
3. Click “Open File” select the “credit-g.arff” file from wherever you saved it.
4. At this point take note of the statistics of the selected attribute. Notice how Weka calculates the minimum, maximum, mean and standard deviation for your quick reference.  
5. Select the check marks next to all of the attributes to select them all.
6. Next click the “Cluster” tab and then select “SimpleKMeans.
7. Now under “Cluster mode” select “Classes to clusters evaluation” and press “Start.”

Your report will include a combination of screenshots and written work. Please ensure your work follows the CSU-Global Guide to Writing and APA Requirements for written work. In your report, please include:

1. A screenshot of Weka Explore when the file “credit-g.arff” is successfully loaded;
2. A snapshot of Weka Explore when the classification is completed
3. After viewing the clustering results, please explain the confusion matrix, e.g. what are the True Positive (TP) number, the True Negative (TN) number, the False Positive (FP) number, and the False Negative (FN) number?
4. Your answer to the following questions: What is the accuracy of the clustering result? (100 words)

 

% Description of the German credit dataset.%% 1. Title: German Credit data%% 2. Source Information%% Professor Dr. Hans Hofmann% Institut f"ur Statistik und "Okonometrie% Universit"at Hamburg% FB Wirtschaftswissenschaften% Von-Melle-Park 5% 2000 Hamburg 13%% 3. Number of Instances:1000%% Two datasets are provided.the original dataset, in the form provided% by Prof. Hofmann, contains categorical/symbolic attributes and% is in the file "german.data".%% For algorithms that need numerical attributes, Strathclyde University% produced the file "german.data-numeric".This file has been edited% and several indicator variables added to make it suitable for% algorithms which cannot cope with categorical variables.Several% attributes that are ordered categorical (such as attribute 17) have% been coded as integer.This was the form used by StatLog.%%% 6. Number of Attributes german: 20 (7 numerical, 13 categorical)%Number of Attributes german.numer: 24 (24 numerical)%%% 7.Attribute description for german%% Attribute 1:(qualitative)%Status of existing checking account%A11 :... <0 DM%A12 : 0 <= ... <200 DM%A13 :... >= 200 DM /%salary assignments for at least 1 year%A14 : no checking account%% Attribute 2:(numerical)%Duration in month%% Attribute 3:(qualitative)%Credit history%A30 : no credits taken/%all credits paid back duly%A31 : all credits at this bank paid back duly%A32 : existing credits paid back duly till now%A33 : delay in paying off in the past%A34 : critical account/%other credits existing (not at this bank)%% Attribute 4:(qualitative)%Purpose%A40 : car (new)%A41 : car (used)%A42 : furniture/equipment%A43 : radio/television%A44 : domestic appliances%A45 : repairs%A46 : education%A47 : (vacation - does not exist?)