Guidelines for the task on data understanding

Data understanding (30 points)
Data semantics (3 points)
Distribution of the variables and statistics (7 points)
Assessing data quality (missing values, outliers) (7 points)
Variables transformations (6 points)
Pairwise correlations and eventual elimination of redundant variables (7 points)

Guidelines for the task on clustering

Clustering Analysis by K-means: (13 points)
1. Choice of attributes and distance function (1 points)
2. Identification of the best value of k (5 points)
3. Characterization of the obtained clusters by using both analysis of the k centroids and comparison of the distribution of variables within the clusters and that in the whole dataset (7 points)
Analysis by density-based clustering (9 points)
1. Choice of attributes and distance function (2 points)
2. Study of the clustering parameters (2 points)
3. Characterization and interpretation of the obtained clusters (5 points)
Analysis by hierarchical clustering (5 points)
1. Choice of attributes and distance function (2 points)
2. Show and discuss different dendograms using different algorithms (3 points)
Final evaluation of the best clustering approach and comparison of the clustering obtained (3 points)

Frequent Pattern Extraction with analysis of different values of support(12 points)
Association Rule Extraction with analysis of different value of support and confidence (12 points)
Discussion on the interesting rules extracted (6 points)