1. DATA MINING –TEXT MINING

2. RETRIEVE DATA SET ROLE NOMINAL TO TEXT PROCESS DOCUMENT TO DATA TOKENIZE FITLER STOPWORDS FILTER TOKENS (Length) TRANSFORM CASE PROCESSES USED (MINING WORD COUNT):

3. TEXT MINING (LOCATING ALL WORDS WITHIN BALLOT QUESTIONS)

4. RESULTS

5. SAME BEGINNING PROCESS AS MINING WORD COUNT ADDITIONS FOR ASSOCIATIONS: 1.NUMERICAL TO BINOMINAL 2.FP-GROWTH 3.CREATE ASSOCIATIONS PROCESSES USED (MINING WORD ASSOCIATIONS):

6. TEXT MINING (CREATING ASSOCIATIONS)

7. RESULTS

8. SAME BEGINNING PROCESS AS MINING WORD COUNT ADDITIONS FOR CLUSTERING: K-Means PROCESSES USED (WORD CLUSTERING):

9. WORD CLUSTERING (CLUSTERING SIMILAR WORDS)

10. RESULTS

11. REFERENCES El Chief’s Youtube page - https://www.youtube.com/channel/UCCvHzQ5AMU6aJYpjS9kOL6g Auburnbigdata blogspot – http://auburnbigdata.blogspot.com/2013/02/simple-model-to-generate- association.html