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Data Mining A.A. 2016/17

DM 1: Foundations of Data Mining

Instructors - Docenti:

Teaching assistant - Assistente:

DM 2: Advanced topics on Data Mining and case studies

Instructors:

News

  • The results of the mid-term held on April 7th, 2017 are out! Link: Results 7.4.2017.
  • The lecture of May 5, 2017 is cancelled due to the review of a European project where the instructors are committed.
  • Erratum: Date for mid-term exam is out: April 7th, 2017 at 11:00 (instead of 9:00) in Rooms A1 + C1.
  • New dates for DM1 oral exams: 2017/02/21 15:00 (3 seats available); 2017/02/23 09:30 (6 seats available) office of Prof. Pedreschi. Please, send an email to BOTH me and Riccardo to book the oral exam.
  • Results DM1 Written Exam 2017/02/08: Results DM1
  • Oral Exam Calendar 2017/02/13 14.00 (seats available), 2017/02/15 14.00 (seats available) office of Prof. Pedreschi. If you need to do the oral exam before the 2017/02/17 but these dates do not fit your timetable please contact us as soon as possible. The next oral exam will be on June.
  • Results DM1 Written Exam 2017/01/19: Results DM1: students who want to do the oral exam the first date available is 2017/01/30 10.00 office of Prof. Pedreschi. Please write an email to anna [dot] monreale [at] unipi [dot] it if you want to do the exam on Monday. Other dates will be scheduled and published on Monday
  • Oral Exam Calendar 2017/01/23 14.00 (seats available), 2017/01/24 14.00 (completed), 2017/01/30 10.00 (seats available) office of Prof. Pedreschi.
  • 2017/01/23: Shtjefni, Cei; 2017/01/24: Inversi, Savasta, Semeraro, Bonfanti, Tanga, Briganti, Di Sarli, Pioli
  • A new project is now available! (see Exam section for details) We recommend to follow the guidelines.
  • Results of the second mid-test of DM1: Results-21Dec2016. For opening the file you need a password that I will send you by email. If you did not receive the email you can require it by email to: anna [dot] monreale [at] unipi [dot] it. RULES: 1) Students having an AVG Mark in the file may do the oral exam. 2) Students having a vote >= 18 in only one of the tests can do the written exam for the part without a sufficient mark. 3) For the oral exam students must come to the written exam and decide together with the teachers the dates of the oral exam. It is possible to do the oral exam also during the written exam.
  • Project deadline extension 23.59 of 10/01/2016
  • Results of the first mid-test of DM1: Results-04Nov2016. For opening the file you need a password that you can require by email to: anna [dot] monreale [at] unipi [dot] it
  • To be included in the course mailing list for urgent communications, please send as soon as possible a mail to anna [dot] monreale [at] unipi [dot] it with the following data: subject= “DM1” and text: name and surname
  • The first project is now available! Details in the Exam Section.

Learning goals -- Obiettivi del corso

… 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

Reading about the "data scientist" job

  • 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

Hours - Orario e Aule

DM 1

Classes - Lezioni

Day of Week Hour Room
Lunedì/Monday 11:00 - 13:00 Aula C
Venerdì/Friday 14:00 - 16:00 Aula A1

Office hours - Ricevimento:

  • Prof. Pedreschi/Monreale: Lunedì/Monday h 14:00 - 16:00, Dipartimento di Informatica

DM 2

Classes - Lezioni

Day of week Hour Room
Tuesday 16:00 - 18:00 B
Friday 16:00 - 18:00 B

Office hours - Ricevimento:

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

Learning Material -- Materiale didattico

Textbook -- Libro di Testo

  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
  • 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

Slides of the classes -- Slides del corso

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"

Past Exams

Data mining software

Class calendar - Calendario delle lezioni (2016-2017)

First part of course, first semester (DMF - Data mining: foundations)

Day Aula Topic Learning material Instructor
1. 19.09.2016 11:00-13:00 C Canceled -
2. 23.09.2016 14:00-16:00 A1 Introduction Course OverviewDM Introduction Monreale
3. 26.09.2016 11:00-13:00 C Data Understanding3.dataunderstanding.pdf 3.data-understanting-appendix.pdf Monreale
4. 30.09.2016 14:00-16:00 A1 Data Preparation 4.data_preparation.pdf Monreale
5. 03.10.2016 11:00-13:00 C Introduction to Python, Knime python_tutorial.zip Monreale/Guidotti
6. 07.10.2016 14:00-16:00 A1 Exercises on Data Understanding. exercises-dm1.pdf Monreale/Guidotti
7. 10.10.2016 11:00-13:00 C Centroid-based methods.dm2014_clustering_intro.pdf dm2014_clustering_kmeans.pdf Monreale
8. 14.10.2016 14:00-16:00 A1 Hierarchical methods.Density Based Clustering dm2014_clustering_hierarchical.pdf knime_slides_mains.pdf Monreale
9. 17.10.2016 11:00-13:00 C Knime - Python: Data Understanding python_data_understanding.zip knime_data_manipulation_iris.zip knime_data_manipulation_adult.zip Monreale/Guidotti
10. 21.10.2016 14:00-16:00 A1 Clustering Validation dm2014_clustering_validation.pdf Monreale
11. 24.10.2016 11:00-13:00 C Knime - Python: Clustering HC with Group Average exercises-clustering.pdf knime_clustering_iris.zip titanic_clustering.ipynb.zip Monreale/Guidotti
12. 28.10.2016 14:00-16:00 A1 Exercises on Clustering HC with Group Average exercises-clustering.pdf Monreale/Guidotti
04.11.2016 9:00-11:00 A First Mid-term test Monreale/Guidotti
13. 07.11.2016 11:00-13:00 C Frequent Patterns & Association Rules 4-5tdm-restructured_assoc.pdf Monreale
14. 11.11.2016 14:00-16:00 A1 Event on Big Data: Aula Magna
15. 14.11.2016 11:00-13:00 C Frequent Patterns & Association Rules
16. 18.11.2016 14:00-16:00 A1 Knime - Python: Frequent Pattern & Association Rules knime_pattern.zip knime_pattern_titanic2.zip titanic_frequent_patterns.ipynb.zip (http://www.borgelt.net/apriori.html)
17. 21.11.2016 11:00-13:00 C Classification chap4_basic_classification.pdf
18. 25.11.2016 14:00-16:00 A1 Classification
19. 28.11.2016 11:00-13:00 C Classification
20. 02.12.2016 14:00-16:00 A1 Exercises on Patterns & Classification
21. 05.12.2016 11:00-13:00 C Canceled
22. 09.12.2016 14:00-16:00 A1 Canceled
23. 12.12.2016 11:00-13:00 C Exercises on Patterns & Classification knime_classification_iris.zip titanic_classification.ipynb.zip Guidotti / Pedreschi
24. 16.12.2016-18.12.2015 A1 Knime - Python: Classification Guidotti / Pedreschi
21.12.2016 9:00-11:00 A Second Mid-term test Monreale/Guidotti

Second part of course, second semester (DMA - Data mining: advanced topics and case studies)

Day Room (Aula) Topic Learning material Instructor (default: Nanni)
1. 21.02.2017 16:00-18:00 B Introduction + Sequential patters/1 Introduction Sequential patters Nanni + Pedreschi
2. 24.02.2017 16:00-18:00 B Sequential patterns/2
3. 28.02.2017 16:00-18:00 B Sequential patterns/3 Link to SPMF, a tool for seq. patterns and sample dataset. Exercises: Text 1 and Text 2
03.03.2017 16:00-18:00 B cancelled
4. 07.03.2017 16:00-18:00 B Time series/1 Time series
5. 10.03.2017 16:00-18:00 B Time series/2 Python examples, Knime examples, link to sounds dataset (source: speech recognition example)
6. 14.03.2017 16:00-18:00 B Time series/3 Python examples/2
7. 17.03.2017 16:00-18:00 B Time series/4 Python examples/3, Knime example
8. 21.03.2017 16:00-18:00 B DM Process/1 Example AMRP (also described in this report, in Italian), CRISP-DM, Link to the CRISP-DM 1.0 guide (by SPSS)
9. 24.03.2017 16:00-18:00 B DM Process/2 Intro_CRM Churn
10. 28.03.2017 16:00-18:00 B DM Process/3 Collective churn analysis, Promotions, Sophistication. Sample reports made by students and (loosely) following CRISP-DM: Report 1 (Italian), Report 2 (English), Report 3 (Italian). Exercise on CRISP-DM: understanding churn
31.03.2017 16:00-18:00 B Cancelled
11. 04.04.2017 16:00-18:00 B Exercises Exercise on Understanding churn (with a solution). See also exercises in section Past Exams
07.04.2017 11:00-13:00 A1 + C1 Mid-term exams
12. 21.04.2017 16:00-18:00 B Classification: alternative methods/1 slides on K-nearest neighbours and Naive Bayes Pedreschi
13. 28.04.2017 16:00-18:00 B Classification: alternative methods/2 slides on Artificial Neural Networks and Support Vector Machines Pedreschi
14. 02.05.2017 16:00-18:00 B Classification: alternative methods/3 slides on ensemble methods and slides on the wisdom of the crowds original 1907 Nature paper by Francis Galton "Vox populi" Pedreschi
15. 05.05.2017 16:00-18:00 Lecture canceled
16. 09.05.2017 16:00-18:00 B Classification: validation methods/1 Slides from P. Adamopoulos, Slides from J.F. Ehmke
17. 12.05.2017 16:00-18:00 B Classification: validation methods/2 Imbalanced data & evaluation, Knime sample classification & evaluation, Python sample classification & evaluation
18. 16.05.2017 16:00-18:00 B Classification: validation methods/3
19. 19.05.2017 16:00-18:00 B Exercises Ex. from past exams 1, Ex. from past exams 2, Mixed Exercises, Lift chart
20. 23.05.2017 16:00-18:00 B Outlier Detection/1 Slides from SDM2010 tutorial
21. 26.05.2017 16:00-18:00 B Outlier Detection/2 Python examples, Knime examples, link to ELKI framework, test dataset for ELKI
22. 30.05.2017 16:00-18:00 B Exercises

Exams

Exam DM part I (DMF)

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 3 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 2016-2017] Project” in the subject. Tasks of the project:
    1. Data Understanding (Assigned on: 17/10/2016): 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: 14/11/2016): 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: 21/11/2016): 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 the hotel type. (see Guidelines for details)
    4. Classification (Assigned on: 12/12/2016): Explore the dataset using classification trees and random forest. Use them to predict the hotel type. (see Guidelines for details)
  • Project 2
    1. Dataset: Expedia (Hotel Recommendations)
    2. Assigned: 11/01/2017
    3. Deadline: 11/02/2017 Deadline extension 23.59 of 13/02/2017
    4. Hint: if the dataset is too big to be analyzed by your computer you can use a representative sample of the entire dataset. You must specify in the project report how you selected this sample and justify your choices.

Guidelines for the project are here.

Exam DM part II (DMA)

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.
  • 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, privacy. The project has to be performed by max 3 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.
    • Sequential patterns. Apply sequential pattern mining (with temporal constraints, if needed) to a dataset that encodes 100 Bach's chorales as sequences of numbers. Two files are provided: one encodes only notes (MIDI pitch integer numbers), the other encodes notes & durations as a single number (number = duration*100 + note). Objective: Find the top-5 most frequent sequences with at least 5 notes, and the top-5 contiguous sequences (i.e. contiguous strings of notes) with at least 4 notes. Repeat the experiments on both the datasets, usign appropriate algorithms and adjusting parameters. Dataset: Preprocessed data, see also the original data and further details on the UCI page.
    • Time series. It is given a dataset of the homicides recorded in the USA over 35 years, expressed as timeseries of yearly counts of homicides for each state. You are asked to look for similarities across the states. Objectives: check whether there is some periodicity in the timeseries; look for clusters over the time series using (i) DBSCAN with DTW, (ii) DBSCAN with Euclidean distance, (iii) K-means with Euclidean distance, each time searching the best parameters and commenting the results. Dataset: download it from Kaggle (11 MB , zipped); you can also (optionally) use the following preprocessing python script to extract the relevant data from the dataset.

Appelli di esame

Mid-term exams

Date Hour Place Notes Marks
First Mid-term 2016 4.11.2016 9:00 - 11:00 Room A
Second Mid-term 2016 21.12.2016 9:00 - 11:00 Room A
Date Hour Place Notes Marks
Mid-term 2017 7.4.2017 11:00 - 13:00 Rooms A1 + C1 Results 7.4.2017

Appelli regolari / Exam sessions

Session Date Time Room Notes Results
1. 19 Jan 2017 09:00 C In the same date we will define the dates for the oral exam.
2. 08 Feb 2017 14:00 C In the same date we will define the dates for the oral exam.
3. In the same date we will define the dates for the oral exam.
4. In the same date we will define the dates for the oral exam.
5. In the same date we will define the dates for the oral exam.
6. In the same date we will define the dates for the oral exam.

Appelli straordinari A.A. 2015/16 / Extra sessions A.A. 2015/16

Date Time Room Notes Results

Edizioni anni precedenti

dm/start.txt · Ultima modifica: 26/05/2017 alle 10:14 (12 ore fa) da Mirco Nanni