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> Dersler  >  Introduction to Data Mining        

CEN 481 Introduction to Data Mining

Course Lecturer: Dr. H. Esin ÜNAL

The topics that will be covered throughout the semester are listed below:


Chapter 1: Introduction
  1. Why Data Mining?
  2. What Is Data Mining?
  3. What Kinds of Data Can Be Mined?
  4. What Kinds of Patterns Can Be Mined?
  5. Which Technologies Are Used?
Chapter 2: Getting to Know Your Data
  1. Data Objects and Attribute Types
  2. Basic Statistical Descriptions of Data
  3. Measuring Data Similarity and Dissimilarity
Chapter 3: Data Preprocessing
  1. Data Cleaning
  2. Data Integration
  3. Data Reduction
  4. Data Transformation and Data Discretization
Chapter 6: Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
  1. Basic Concepts
  2. Frequent Itemset Mining Methods
  3. Which Patterns Are Interesting?—Pattern Evaluation Methods
Chapter 8: Classification: Basic Concepts and Methods
  1. Decision Tree Induction
  2. Bayes Classification Methods
  3. Rule-Based Classification
  4. Model Evaluation and Selection
Chapter 10: Cluster Analysis: Basic Concepts and Methods
  1. Cluster Analysis
  2. Partitioning Methods
  3. Hierarchical Methods
  4. Density-Based Methods
  5. Grid-Based Methods
Chapter 12: Outlier Detection
  1. Outliers and Outlier Analysis
  2. Outlier Detection Methods
Chapter 13: Data Mining Trends and Research Frontiers


The textbooks of the course:
  1. "Data Mining: Concepts and Techniques", by Jiawei Han, Micheline Kamber and Jian Pei (3rd edition).
  2. "Introduction to Data Mining" by Pang-Ning Tan, Michael Steinbach and Vipin Kumar.
  3. "Data Mining – Practical Machine Learning Tools and Techniques" by Ian H. Witten, Eibe Frank and Mark A. Hall.

Weekly lecture slides:
  1. Week 1
  2. Week 2
  3. Week 3
  4. Week 4 - Week 4 Examples
  5. Week 5 - Week 5 Examples
  6. Week 6 - Week 6 Examples
  7. There is no lecture on 28.10.2019 (Holiday)
  8. MID-TERM EXAM (4.11.2019 - 13:15)
  9. Week 9 - Week 9 Examples
  10. Week 10 - Week 10 Examples
  11. Week 11 (Instruction for the homework)
  12. Week 12
  13. Week 13-14-15 (Presentations on Data Mining Trends and Research Frontiers)


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