Data-Mining-2020-2021-Project-Group24-customer_supermarket

1. Data Understanding and Preparation

• 1.1 Data semantics
• 1.2 Assessing data quality
• 1.3 Distribution of the variables and statistics
• 1.4 Variables transformations and generation
• 1.5 Preliminary observations
• 1.6 Exploring the new features for a statistical analysis

2. Clustering Analysis

• 2.1 K-means
• 2.2 Density based clustering
• 2.3 Hierarchical clustering
• 2.4 K-Medoids (optional task)
• 2.5 Final evaluation of the best clustering approach and comparison of the clustering obtained

3. Classification

• 3.1 Data preparation
• 3.2 Performing prediction using various different classification algorithms
• 3.3 Model comparisons

4. Sequential Pattern Mining

• 4.1 Data preparation
• 4.2 Mining sequential patterns
• 4.3 Results discussion and mining sequential patterns through categorizing products
• 4.4 Sequential pattern mining with temporal constraints

5. Conclusion