Instructors - Docenti:

• Dino Pedreschi
  • KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa
  • http://www-kdd.isti.cnr.it
  • dino [dot] pedreschi [at] unipi [dot] it

Teaching assistant - Assistente:

• Riccardo Guidotti
  • KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
  • guidotti [at] di [dot] unipi [dot] it

Instructors:

• Mirco Nanni, Dino Pedreschi
  • KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
  • http://www-kdd.isti.cnr.it
  • mirco [dot] nanni [at] isti [dot] cnr [dot] it
  • dino [dot] pedreschi [at] unipi [dot] it

Instructors:

• Dino Pedreschi, Anna Monreale
  • KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
  • http://www-kdd.isti.cnr.it
  • mirco [dot] nanni [at] isti [dot] cnr [dot] it
  • dino [dot] pedreschi [at] unipi [dot] it
  • anna [dot] monreale [at] unipi [dot] it

Teaching assistant - Assistente:

• Riccardo Guidotti
  • KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
  • guidotti [at] di [dot] unipi [dot] it

• Next Exam Session: September 13, 9:00, room A1.
• Oral Exam DM1: In Sept a possible date for oral exam of DM1 will be on Sept 5 if you have already made the written part. Please, send me an email at anna [dot] monreale [at] unipi [dot] it in case you want to do the oral exam.
• ERRATA CORRIGE: The results of the written exam for DM2 of June 12 have been updated (a few marks slightly increased): DM2 Results.
• The results of the written exam for DM2 of June 12 are available: DM2 Results.
• The results of the (definitely too easy) second midterm exam for DM2 (June 1) are available: Results.
• DM2 projects: the exercises on classification and outlier detection are out.
• The results of the first midterm exam for DM2 (April 10) are available: Results.
• DM2 projects: the exercises on time series and sequential patterns are out.
• DM2: first mid-term exam fixed to April 10 from 16:00 to 18:00, room E.
• Today (March 1st, 2018)'s class is cancelled due to meteo conditions.
• Last date for oral exam in winter session: 23 Feb. 2018 please book here: https://doodle.com/poll/a9y85z3ztmvz2grr (max 6 people)
• Next dates for oral exam: a) 29 Jan 9:00-13:00 Pedreschi's office; b) 30 Jan 9:00-13:00 Monreale's office; c) 06 Feb 9:00-13:00 Room of written exam; d) 09 Feb 9:00-13:00 Monreale's office. e) 14 Feb 9:00-13:00 Monreale's office.Please fill the doodle: https://doodle.com/poll/qp42cqcbi4cy95f9
• The Results of the written exam on 17.01.2018 are online results-2018-1-17.pdf
• Mid-term Exam question time: Wed 15th November 16.00-17.00 in Anna Monreale's office.
• The Results of the first mid-term are online 2017-10-30-first-midterm.pdf. For any problem and question about the written exam please contact Riccardo Guidotti and Anna Monreale.
• Please, fill the doodle about the project group. In the field of Participant you should insert the list of surnames of the group components. I sent the doodle link by email. If you do not have access to the DM group please send me an email following the below instructions.
• Please, send to anna [dot] monreale [at] unipi [dot] it an email with:
  1. subject: DATA MINING
  2. content: your name, your surname, your studentID, the credits of your exam (12CFU, 6CFU, 9CFU)

… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.

Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.

La grande disponibilità di dati provenienti da database relazionali, dal web o da altre sorgenti motiva lo studio di tecniche di analisi dei dati che permettano una migliore comprensione ed un più facile utilizzo dei risultati nei processi decisionali. L'obiettivo del corso è quello di fornire un'introduzione ai concetti di base del processo di estrazione di conoscenza, alle principali tecniche di data mining ed ai relativi algoritmi. Particolare enfasi è dedicata agli aspetti metodologici presentati mediante alcune classi di applicazioni paradigmatiche quali il Basket Market Analysis, la segmentazione di mercato, il rilevamento di frodi. Infine il corso introduce gli aspetti di privacy ed etici inerenti all’utilizzo di tecniche inferenza sui dati e dei quali l’analista deve essere a conoscenza. Il corso consiste delle seguenti parti:

1. i concetti di base del processo di estrazione della conoscenza: studio e preparazione dei dati, forme dei dati, misure e similarità dei dati;
2. le principali tecniche di datamining (regole associative, classificazione e clustering). Di queste tecniche si studieranno gli aspetti formali e implementativi;
3. alcuni casi di studio nell’ambito del marketing e del supporto alla gestione clienti, del rilevamento di frodi e di studi epidemiologici.
4. l’ultima parte del corso ha l’obiettivo di introdurre gli aspetti di privacy ed etici inerenti all’utilizzo di tecniche inferenza sui dati e dei quali l’analista deve essere a conoscenza

• Data, data everywhere. The Economist, Feb. 2010 download
• Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 link
• Welcome to the yotta world. The Economist, Sept. 2011 download
• Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 link
• Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 link
• Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics download
• Peter Sondergaard, Gartner, Says Big Data Creates Big Jobs. Oct 22, 2012: YouTube video

• Towards Effective Decision-Making Through Data Visualization: Six World-Class Enterprises Show The Way. White paper at FusionCharts.com. download

Classes - Lezioni

Office hours - Ricevimento:

• Prof. Pedreschi: Lunedì/Monday h 14:00 - 16:00, Dipartimento di Informatica
• Prof. Monreale: Giovedì/Thursday h 14:00 - 16:00, Dipartimento di Informatica
• Dr. Guidotti: Mercoledì/Wednesday h 16:00 - 18:00, Dipartimento di Informatica

Classes - Lezioni

Office hours - Ricevimento:

• Nanni : appointment by email, c/o ISTI-CNR

• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN ISBN-13: 978-0-13-312890-1, 2018 (Second Edition)
  • http://www-users.cs.umn.edu/~kumar/dmbook/index.php
  • I capitoli 4, 6, 8 sono disponibili sul sito del publisher. – Chapters 4,6 and 8 are also available at the publisher's Web site.
• Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. GUIDE TO INTELLIGENT DATA ANALYSIS. Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7
• Laura Igual et al. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. 1st ed. 2017 Edition.
• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data. 1st Edition.

• The slides used in the course will be inserted in the calendar after each class. Most of them are part of the the slides provided by the textbook's authors Slides per "Introduction to Data Mining".

Le slide utilizzate durante il corso verranno inserite nel calendario al termine di ogni lezione. In buona parte esse sono tratte da quelle fornite dagli autori del libro di testo: Slides per "Introduction to Data Mining"

* Some text of past exams on DM1 (6CFU):

• 2017-1-19.pdf, 2017-9-6.pdf, 2016-05-30-dm1-seconda.pdf

* Some solutions of past exams containing exercises on KNN and Naive Bayes classifiers DM1 (9CFU):

• dm2_exam.2017.06.13_solutions.pdf, dm2_exam.2017.07.04_solutions.pdf, dm2_mid-term_exam.2017.06.06_solutions.pdf

* Some exercises (partially with solutions) on sequential patterns and time series can be found in the following texts of exams from the last years:

• dm2_exam.2015.04.13.results.pdf, dm2_exam.2016.04.4_sol.pdf, dm2_exam.2016.04.5_sol.pdf, dm2_exam.2016.06.20_sol.pdf, dm2_exam.2016.07.08_sol.pdf

• Some very old exercises (part of them with solutions) are available here, most of them in Italian, not all of them on topics covered in this year program:
  • Verifica 2006, Verifica 2005 (con soluzioni), Verifica 2004
  • Verifica 5 giugno 2007, Verifica 26 giugno 2007, Verifica 24 luglio 2007 (e Soluzioni)
  • Verifica 3 aprile 2008 (e Soluzioni), Verifica 18 luglio 2008 - parte 1, Verifica 18 luglio 2008 - parte 2
  • Exam with solution 2010-06-01 Exam with solution 2010-06-22 Exam with solution 2010-09-09 Exam with solution 2010-07-13

• KNIME The Konstanz Information Miner. Download page
• Python - Anaconda (2.7 version!!!): Anaconda is the leading open data science platform powered by Python. Download page (the following libraries are already included)
• Scikit-learn: python library with tools for data mining and data analysis Documentation page
• Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Documentation page
• WEKA Data Mining Software in JAVA. University of Waikato, New Zealand Download page

The exam is composed of three parts:

• A written exam, with exercises and questions about methods and algorithms presented during the classes. It can be substitute with the first and second mid-term tests of November and December.

• An oral exam, that includes: (1) discussing the project report with a group presentation; (2) discussing topics presented during the classes, including the theory of the parts already covered by the written exam.

• A project consists in exercises that require the use of data mining tools for analysis of data. Exercises include: data understanding, clustering analysis, frequent pattern mining, and classification. The project has to be performed by max 4 people. It has to be performed by using Knime, Python or a combination of them. The results of the different tasks must reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The project must be delivered at least 2 days before the oral exam. The paper must emailed to datamining [dot] unipi [at] gmail [dot] com. Please, use “[DM 2017-2018] Project” in the subject. Tasks of the project:
  1. Data Understanding (Assigned on: 03/10/2017): Explore the dataset with the analytical tools studied and write a concise “data understanding” report describing data semantics, assessing data quality, the distribution of the variables and the pairwise correlations.
  2. Clustering analysis (Assigned on: 14/11/2017): Explore the dataset using various clustering techniques. Carefully describe your's decisions for each algorithm and which are the advantages provided by the different approaches. (see Guidelines for details)
  3. Association Rules (Assigned on: 21/11/2017): Explore the dataset using frequent pattern mining and association rules extraction. Then use them to predict a variable either for replacing missing values or to predict if an employee will leave prematurely or not. (see Guidelines for details)
  4. Classification (Assigned on: 12/12/2017): Explore the dataset using classification trees and random forest. Use them to predict if an employee will leave prematurely or not. (see Guidelines for details)

• Project 1
  1. Dataset: HRA (Human Resources Analytics)
  2. Assigned: 29/09/2017
  3. Deadline: 06/01/2018
  4. Link: https://www.kaggle.com/ludobenistant/hr-analytics/

Guidelines for the project are here.

The exam is composed of three parts:

• A written exam, with exercises and questions about methods and algorithms presented during the classes. It can be substitute with the first and second mid-term tests of April and June.

• A small online test for the data ethics part. The test can be taken at the following link: Link to "First Aid for Data Scientist" web site (pwd: datamining_2018). Register, and enroll to the “First Aid for Data Scientist” course. Take the quizzes of the 3 units. Then, download your certificate and send it to mirco [dot] nanni [at] isti [dot] cnr [dot] it before the oral exam.

• An oral exam, that includes: (1) discussing the project report with a group presentation; (2) discussing topics presented during the classes, including the theory of the parts already covered by the written exam.

• A project consists in exercises that require the use of data mining tools for analysis of data. Exercises include: sequential patterns, time series, classification (alternative methods and validation), outlier detection. The project has to be performed by max 3 people. It has to be performed by using Knime, Python, other software or a combination of them. The results of the different tasks must reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The project must be delivered at least 2 days before the oral exam:
  • Time series: given the 50+ years long history of stock values of a company, split it into years, and study their similarities, also using clustering. Objectives: compare similarities, compute clustering. Dataset: IBM stocks (source: Yahoo Finance), includes a Python snippet to read and split the data. Dataset obtained from Yahoo!Finance service.
  • Sequential patterns: discover patterns over the stock value time series above. Before that, preprocess the data by splitting it into monthly time series and discretizing them in some way. Objective: find Motifs-like patterns (i.e. frequent contiguous subsequences) of length at least 4 days. Dataset: same as the point before.
  • (Alternative) Classification methods: test different classification methods over a simple classification problem. Dataset: the UCI Abalone dataset, containing various features of abalones, including the age – to be inferred by the number or rings. Objective: (i) discard the “Infant” abalones; (ii) discretize the attribute “Number of rings” into 2 classes; (iii) try at least 3 different classification methods (among those discussed in DM2, including ensemble methods) on the resulting dataset, using the discretized n. of rings as class, and evaluating them with cross-validation.
  • Outlier detection: from the Abalone dataset used above, identify the top 1% outliers. Objective: adopt at least two different methods belonging to different families (i.e. model-based, distance-based, density-based, angle-based, …) to identify the 1% of input records with the highest likelihood of being outliers, and compare the results. Dataset: same as the point before.

• Data Mining A.A. 2017/18
• Data Mining A.A. 2016/17
• Data Mining A.A. 2015/16
• Data Mining A.A. 2014/15
• Data Mining A.A. 2013/14
• Data Mining A.A. 2012/13
• Data Mining A.A. 2011/12
• Data Mining A.A. 2010/11
• Data Mining A.A. 2009/10
• Data Mining A.A. 2008/09
• Data Mining A.A. 2007/08
• Data Mining A.A. 2006/07
• PhD Workshop 2011
• Analisi delle Reti Sociali
• Social and Information Networks
• ICT for BI & CRM - Part III: Data Mining
• Data Mining for Customer Relationship Management 2015
• Data Mining for Customer Relationship Management 2016
• Data Mining for Customer Relationship Management 2017
• Instructions for camera ready and copyright transfer