#acl DataWarehouseEDataMiningModulo2/ReadWriteGroup:read,write,admin,revert,delete All:read == Data Warehouse e Data Mining (Modulo 2) == <> === Informazioni sul corso === '''Nome Docente''': Pasquale Rullo. '''Orario di ricevimento''': su appuntamento. '''Assistente''': Ettore Ritacco '''Orario di ricevimento''': su appuntamento. '''[[DataWarehouseEDataMiningModulo2SUA|Scheda descrittiva del corso]]''' <
> === Avvisi === * '''07/05/2015''' * Lab info: on 12/05/2015 h11.30-13.30, Mario Ettorre, Marketing & Sales Director of Exeura (http://www.exeura.eu/), will hold a seminar titled '''From BI to Big Data Analytics: market evolution, technologies, methodologies and solutions for vertical markets'''. The lab lesson will be postponed to Friday, 15th April h11.30. '''TITOLO''': Dalla BI ai Big Data Analytics: evoluzione del mercato, tecnologie, metodologie e soluzioni per i vertical market '''ABSTRACT''': L’avvento dei Big Data ha rivoluzionato il mondo dell’analisi dei dati richiedendo tecnologie, approcci e metodologie “disruptive”. Nel corso del seminario si illustrerà l’evoluzione rilevata nel corso degli ultimi venti anni nel panorama della “Data Analysis" che, partendo dai data warehouse, è giunta oggi ai “Big Data Analytics attraversando l’importante tappa della Business Intelligence.In particolare si mostreranno gli impatti e le opportunità offerte dei Big Data nei vai mercati verticali focalizzando l’attenzione sul tema del Customer Lifecycle Management. Il seminario prevede la condivisione di demo live di prodotti e soluzioni industriali costruite sul prodotto Rialto. * '''21/04/2015''' * Lab info: today's lesson will be postponed to Friday, 24th April h11.30 <
> === Teaching material === ==== Slides ==== * Lesson 1 - Introduction [ [[attachment:rullo - lesson1.pdf]] ] * Lesson 2 - Concept Learning [ [[attachment:Lesson2 - Concept Learning.pdf]] ] * Lesson 3 - Beyond Candidate Elimination [ [[attachment:rullo - lesson 3 - beyond CE-1.pdf]] ] * Lesson 4/5 - Decision Tress [ [[attachment:Less5-Decision Trees.pdf]] ] * Lesson 6 - Naive Bayes [ [[attachment:Less7 - NB Classifiers.pdf]] ] * Lesson 7 - Rule-Based Classification [ [[attachment:Less6- RuleBased Classifiers v1.pdf]] ] * Lesson 8 - Instance-Based Classification [ [[attachment:Less8-InstanceBasedClassifiers v1.pdf]] ] * Lesson 9 - Text Classification - Clustering (K-Means) [ [[attachment:Less9 - Text Classification.pdf]] ] [ [[attachment:Less12-Clustering-Kmeans.pdf]] ] * Lesson 10 - Association Rules [ [[attachment:Less13-Association Rules - A priori v1.pdf]] ] ==== Lab Activites ==== Lesson 1 [ [[attachment:01.Introduction.pdf]] ] * Introduction to the Data Mining * DIKW model * CRISP-DM Methodology Lesson 2 [ [[attachment:2. Data Understanding.pdf]] ] , [ [[attachment:3. Data Preparation.pdf]] ] * Data analysis * Data manipulation Lesson 3 [ [[attachment:4. Study case - Drug.pdf]] ], [ [[attachment:drug.arff]] ] * Weka * A study case for data understanding and manipulation - Drug Lesson 4 [ [[attachment:5. Study case - Churn Analysis.pdf]] ], [ [[attachment:churn.arff]] ], [ [[attachment:sick.arff]] ] * A study case for data understanding and manipulation - Churn * A study case for data understanding and manipulation - Sick Lesson 5 [ [[attachment:06. Study case - Image Segmentation.pdf]] ], [ [[attachment:segment.arff]] ] * Exam simulation - A study case for data understanding and manipulation - Image Segmentation Lesson 6 [ [[attachment:07. Evaluation - part 1.pdf]] ], [ [[attachment:08. Study case - Intrusion Detection.pdf]] ], [ [[attachment:kddcup99-sample.zip]] ] * Evaluation (part 1) * Exam simulation - A study case for data understanding and manipulation - Image Segmentation Lesson 7 [ [[attachment:07. Evaluation.pptx]] ], [ [[attachment:09. Exercises - DT & NB.pptx]] ] * Evaluation (final) * Exercises: Decision Trees and Naive Bayes Lesson 8 [ [[attachment:Project.pdf]] ], [ [[attachment:Training set.zip]] ] * Project description * Project's training data Lesson 9 [ [[attachment:10. Exercises - RL & IBC - Errata Corrige.pptx]] ] * Exercises: Rule Learning and Instance-Based Classification * '''Updated version of the lesson''' (18th May 2015) Lesson 10 [ [[attachment:Test set.zip]] ] * Final evaluation: in attachment the test set