Obiettivi e programma formativo
Collegio dei docenti
Linee di ricerca
Lezioni
Dottorandi
Data Mining
Knowledge Discovery in Databases: the process and the CRISP-DM methodology
Rule-based classification
(see also Chapter 2 of the text by T. Mitchell,
Machine Learning
, Morgan Kaufmann, 1997,
1
A survey on the separate-and-conquer approach to rule-based learning)
Decision trees
(see also: Chapter 3 of the text by T. Mitchell,
Machine Learning
, Morgan Kaufmann, 1997;
1
,
2
for a survey on decision tree learning;
3
,
4
,
5
for the simplification of decision trees)
Bayesian framework for classification
(see also: Chapter 6 of the text by T. Mitchell,
Machine Learning
, Morgan Kaufmann, 1997;
Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression
,
Article on hierarchical text categorization
)
Parametric and non parametric regression
;
Stepwise Model Tree Induction
(see also Sections 1.2, 2.1, 2.3, 4.6 of the text by A. Azzalini & B. Scarpa,
Analisi dei dati e Data Mining
. Springer, 2004;
this paper
for model trees)
Variable Associations
(see also Chapter 6 of the text by A. Azzalini & B. Scarpa,
Analisi dei dati e Data Mining
. Springer, 2004;
1
for a perspective on the relation between association measures and association rules;
2
for a seminal paper on mining association rules)
Relational Data Mining
(see also
1
for an introduction on Multi-Relational Data Mining, and
2
for efficiency issues)
Relational Classification Tools
(see also
FOIL
,
FOIL vs Related Systems
,
Progol
and
TILDE
)
