Data Mining

Course Description:

The Data Mining course is designed to provide students with a comprehensive understanding of data mining techniques and their applications in extracting valuable insights and patterns from large datasets. The course covers the fundamentals of data mining, explores various data mining algorithms, and teaches students how to preprocess data, apply different mining techniques, and evaluate and interpret the results. Students will learn how to identify patterns, make predictions, and discover hidden relationships in data using popular data mining tools and software.

Course Objectives:

1. Understand the fundamentals of data mining and its applications.
2. Learn about different types of data mining techniques, including classification, clustering, association rule mining, and anomaly detection.
3. Develop skills in preprocessing and preparing data for data mining tasks.
4. Understand the process of selecting and applying appropriate data mining algorithms for different tasks.
5. Learn how to evaluate and interpret the results of data mining models.
6. Explore advanced techniques for feature selection, dimensionality reduction, and handling imbalanced datasets.
7. Apply data mining techniques to real-world datasets and effectively communicate the results.

Course Outline:

Module 1: Introduction to Data Mining

– Overview of data mining and its applications
– Understanding the data mining process and terminology

Module 2: Data Preprocessing and Cleaning

– Data cleaning and handling missing values
– Data transformation and normalization
– Feature selection and dimensionality reduction

Module 3: Classification Techniques

– Decision tree-based algorithms (e.g., C4.5, CART)
– Naive Bayes classifier
– k-Nearest Neighbors (k-NN)
– Support Vector Machines (SVM)
– Evaluation metrics for classification models

Module 4: Clustering Techniques

– k-Means clustering
– Hierarchical clustering
– Density-based clustering (e.g., DBSCAN)
– Evaluation metrics for clustering models

Module 5: Association Rule Mining

– Apriori algorithm
– FP-Growth algorithm
– Evaluation metrics for association rule mining

Module 6: Anomaly Detection

– Statistical-based anomaly detection
– Distance-based anomaly detection
– Density-based anomaly detection
– Evaluation metrics for anomaly detection models

Module 7: Feature Selection and Dimensionality Reduction

– Filter and wrapper methods for feature selection
– Principal Component Analysis (PCA)
– t-SNE (t-Distributed Stochastic Neighbor Embedding)

Module 8: Handling Imbalanced Datasets

– Techniques for handling imbalanced datasets
– Oversampling and undersampling methods
– Cost-sensitive learning

Module 9: Advanced Topics in Data Mining

– Text mining and sentiment analysis
– Time series analysis and forecasting
– Web mining and social network analysis

Module 10: Real-World Applications and Projects

– Applying data mining techniques to real-world datasets and projects
– Hands-on projects and simulations to reinforce learning