Data Mining 

Day 1: Foundations and Data Preparation 
Module 1: Introduction to Data Mining 

What is Data Mining

Importance and Applications of Data Mining

Types of Data (Structured, Semi-structured, Unstructured)

Data Objects and Attribute Types (Nominal, Ordinal, Numeric)

Measuring Data Similarity and Dissimilarity

Introduction to Data Visualisation Techniques

 

Module 2: Data Preprocessing Essentials 

Overview of Data Preprocessing

Data Integration and Aggregation Techniques

Data Cleaning Strategies (Missing Values, Noisy Data)

Data Reduction Techniques (Sampling, PCA, Feature Selection)

Data Transformation (Normalisation, Scaling, Encoding Categorical Data)

Data Discretisation Methods (Binning, Histogram Analysis)

 

Module 3: Data Warehousing and Online Analytical Processing (OLAP) 

Basic Concepts of Data Warehousing

Introduction to Data Cubes and OLAP Operations

Multidimensional Data Models (Star Schema, Snowflake Schema)

Data Warehouse Design and Architecture

Implementing and Managing Data Warehouses

Practical Uses and Applications of Data Warehousing and OLAP

 

Module 4: Mining Frequent Patterns and Associations 

Concept of Frequent Pattern Mining

Frequent Itemset Mining Algorithms (Apriori Algorithm, FP-Growth)

Association Rule Generation and Interpretation

Evaluation of Patterns (Support, Confidence, Lift, Conviction)

Real-world Application Scenarios and Examples (Market Basket Analysis)

Day 2: Advanced Analytics & Real-world Applications
Module 5: Classification Techniques and Algorithms

Introduction and Purpose of Classification

Decision Tree Induction (CART, ID3, C4.5)

Bayesian Classification (Naïve Bayes Classifier)

Rule-Based Classification Methods

Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1-Score

Practical examples using real-world datasets

 

Module 6: Advanced Classification Methods 

Introduction to Neural Networks and Backpropagation (brief overview)

Frequent Pattern-based Classification Concepts

Lazy Learners (k-Nearest Neighbours - kNN)

Overview: Genetic Algorithms, Rough Set Approach, Fuzzy Logic (brief, conceptual coverage)

Strengths and Weaknesses of Advanced Classification Techniques

 

Module 7: Cluster Analysis Methods 

Understanding Cluster Analysis and Applications

Partitioning Methods (k-Means and k-Medoids Clustering)

Hierarchical Clustering (Agglomerative, Divisive)

Density-based Clustering Methods (DBSCAN)

Grid-based Clustering Methods (Conceptual overview)

Evaluating and Visualising Cluster Results

 

Module 8: Outlier Detection & Advanced Clustering Techniques 

Understanding Outliers and Importance of Detection

Statistical Approaches to Outlier Detection

Proximity-based Outlier Detection (Distance-based, Density-based)

Clustering-based and Classification-based Outlier Detection

Overview of Advanced Clustering: Probabilistic Model-Based Clustering (Gaussian Mixtures)

High-dimensional Data Clustering and Challenges (brief overview)