1. Introduction to Data Mining 2015/8/101ahc@Data Engineering Group in ACL

2. Outline Introduction Learning Methods Applications Conclusion 2015/8/10ahc@Data Engineering Group in ACL2

3. Introduction Massive data/information in the real world Induced rules/procedures for management ◦ Decreasing human labor ◦ Increasing process efficiency 2015/8/10ahc@Data Engineering Group in ACL3

4. In computer sciences… Machine Learning Data Mining Artificial Intelligence Gene algorithm …. 2015/8/10ahc@Data Engineering Group in ACL4

5. Machine Learning I/III Unsupervised learning ◦ Given points that are drawn from a common distribution (unlabeled) ◦ The goal is to find the interesting distribution ◦ Clustering, Dimension Reduction, …  K-means, Neural Network, … 2015/8/10ahc@Data Engineering Group in ACL5

6. Machine Learning II/III Supervised learning ◦ Given points that are known/labeled ◦ The goal is to find the mapping function ◦ Classification, Handwriting Recognition,…  Support Vector Machines, Decision Tree,.. 2015/8/10ahc@Data Engineering Group in ACL6

7. Machine Learning III/III Semi-supervised learning ◦ Given both kinds of points mentioned above ◦ The goal is to find the mapping function  Unlabeled points are used for adjustment ◦ In the environment of fewer (labeled) information,… 2015/8/10ahc@Data Engineering Group in ACL7

8. Associative Rule Learning Discovering interesting relations between variables in large databases Classification, Bioinformatics,.. ◦ Apriori,.. 2015/8/10ahc@Data Engineering Group in ACL8 MilkBreadT-shirtShoes Y YY YYY YY Rule: Milk Bread

9. Applications in the real world Text Classification Document Classification Web Page Classification … 2015/8/10ahc@Data Engineering Group in ACL9

10. Standard Classification Procedure Preprocessing ◦ Content Retrieving ◦ Sentence Segmentation (in Chinese) ◦ Feature Selection Classification ◦ Model Generation ◦ Prediction Learning ◦ Active Learning 2015/8/10ahc@Data Engineering Group in ACL10

11. Lazy Learning The inductive process is delayed until classification Taking advantage of related and qualitative evidence Able to model complex decision spaces ◦ J. Han and M. Kamber, Data Mining: Concepts and Techniques, Elsevier, 2006. 2015/8/10ahc@Data Engineering Group in ACL11

12. Conclusion Machine Learning and Data Mining techniques are handling massive information Observing issues in special domains 2015/8/10ahc@Data Engineering Group in ACL12