1. Data Mining By: Johan Johansson

2. Mining Techniques Association Rules Association Rules Decision Trees Decision Trees Clustering Clustering Nearest Neighbor Nearest Neighbor Neural Networks Neural Networks

3. Association Rules If the confidence for Bread -> Milk is 80% then it does NOT imply the Milk -> Bread has a confidence of 80%. If the confidence for Bread -> Milk is 80% then it does NOT imply the Milk -> Bread has a confidence of 80%. A good rule has both high confidence and high support. A good rule has both high confidence and high support.

4. Decision Trees Produces rules that are mutually exclusive as opposed to Association rules where there might be a lot of redundancy. Produces rules that are mutually exclusive as opposed to Association rules where there might be a lot of redundancy. Rule induction goes from the bottom up and collect all possible patterns that are interesting. Rule induction goes from the bottom up and collect all possible patterns that are interesting. Decision trees do a greedy search, looking for the best possible split on the next step. Decision trees do a greedy search, looking for the best possible split on the next step.

5. K-Nearest Neighbors Instead of selecting the nearest neighbor, let’s take a “vote” from the K nearest neighbors. Instead of selecting the nearest neighbor, let’s take a “vote” from the K nearest neighbors. Larger values of K reduce the effect of noise on the classification, but make boundaries between classes less distinct. Larger values of K reduce the effect of noise on the classification, but make boundaries between classes less distinct.

6. K-Nearest Neighbors

7. Main differences between Clustering and Nearest Neighbor Clustering is mostly used for consolidation while Nearest Neighbor is also used for predictions. Clustering is mostly used for consolidation while Nearest Neighbor is also used for predictions. Nearest Neighbor uses supervised learning, which is defined by the problem so it can make accurate and relevant predictions. Nearest Neighbor uses supervised learning, which is defined by the problem so it can make accurate and relevant predictions.

8. Neural Networks Mimic the structure and functioning of the human brain. Mimic the structure and functioning of the human brain. Using sophisticated pattern detection and machine learning algorithms. Using sophisticated pattern detection and machine learning algorithms. Hard to use and hard to implement but very effective. Hard to use and hard to implement but very effective.

9. Simple Neural Network

10. Simple Neural Network (cont’d) Age and income (nodes) is based on fuzzy logic, where is the values are between 0 and 1, inclusive. Age and income (nodes) is based on fuzzy logic, where is the values are between 0 and 1, inclusive. The weights (links) determines the output, which is between 0 and 1, inclusive. The weights (links) determines the output, which is between 0 and 1, inclusive. 0.47(0.7) + 0.65(0.1) = 0.39 which is closer to 0 than 1 so it’s not the “default” in this case. 0.47(0.7) + 0.65(0.1) = 0.39 which is closer to 0 than 1 so it’s not the “default” in this case.

11. References http://www.thearling.com/text/dmtechniques/ dmtechniques.htm http://www.thearling.com/text/dmtechniques/ dmtechniques.htm http://www.thearling.com/text/dmtechniques/ dmtechniques.htm http://www.thearling.com/text/dmtechniques/ dmtechniques.htm http://en.wikipedia.org/wiki/Data_mining http://en.wikipedia.org/wiki/Data_mining http://en.wikipedia.org/wiki/Data_mining