Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- dm:start [20/05/2019 alle 14:38 (5 anni fa)] – [Second part of course, second semester (DMA - Data mining: advanced topics and case studies)] Mirco Nanni
+++ dm:start [16/09/2024 alle 06:41 (4 giorni fa)] (versione attuale) – Riccardo Guidotti
@@ Linea 1: / Linea 1: @@
-<html>
+====== Data Mining A.A. 2024/25 ======
-<!-- Google Analytics -->
-<script type="text/javascript" charset="utf-8">
-(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
-ga('create', 'UA-34685760-1', 'auto', 'personalTracker', {'allowLinker': true});
+===== DM1 - Data Mining: Foundations (6 CFU) =====
-ga('personalTracker.require', 'linker');
-ga('personalTracker.linker:autoLink', ['pages.di.unipi.it', 'enforce.di.unipi.it', 'didawiki.di.unipi.it'] );
-ga('personalTracker.require', 'displayfeatures');
-ga('personalTracker.send', 'pageview', 'ruggieri/teaching/dm/');
-setTimeout("ga('send','event','adjusted bounce rate','30 seconds')",30000);
-</script>
-<!-- End Google Analytics -->
-<!-- Capture clicks -->
-<script>
-jQuery(document).ready(function(){
-  jQuery('a[href$=".pdf"]').click(function() {
-    var fname = this.href.split('/').pop();
-    ga('personalTracker.send', 'event',  'DM', 'PDFs', fname);
-  });
-  jQuery('a[href$=".r"]').click(function() {
-    var fname = this.href.split('/').pop();
-    ga('personalTracker.send', 'event',  'DM', 'Rs', fname);
-  });
-  jQuery('a[href$=".zip"]').click(function() {
-    var fname = this.href.split('/').pop();
-    ga('personalTracker.send', 'event',  'DM', 'ZIPs', fname);
-  });
-  jQuery('a[href$=".mp4"]').click(function() {
-    var fname = this.href.split('/').pop();
-    ga('personalTracker.send', 'event',  'DM', 'Videos', fname);
-  });
-  jQuery('a[href$=".flv"]').click(function() {
-    var fname = this.href.split('/').pop();
-    ga('personalTracker.send', 'event',  'DM', 'Videos', fname);
-  });
-});
-</script>
-</html>
-====== Data Mining A.A. 2018/19 ======
-===== DM 1: Foundations of Data Mining (6 CFU) =====
+Instructors:
-Instructors - Docenti:
   * **Dino Pedreschi**
-    * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa
+    * KDDLab, Università di Pisa
     * [[http://www-kdd.isti.cnr.it]]
     * [[dino.pedreschi@unipi.it]]
-Teaching assistant - Assistente:
   * **Riccardo Guidotti**
-    * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
+    * KDDLab, Università di Pisa
-    * [[guidotti@di.unipi.it]]
+    * [[https://kdd.isti.cnr.it/people/guidotti-riccardo]]
+    * [[riccardo.guidotti@di.unipi.it]]
-===== DM 2: Advanced topics on Data Mining and case studies (6 CFU) =====
+Teaching Assistant
+  * **Andrea Fedele**
+    * KDDLab, Università di Pisa
+    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]]
+    * [[andrea.fedele@phd.unipi.it]]
+===== DM2 - Data Mining: Advanced Topics and Applications (6 CFU) =====
 Instructors:
-  * **Mirco Nanni, Dino Pedreschi**
-    * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
-    * [[http://www-kdd.isti.cnr.it]]
-    * [[mirco.nanni@isti.cnr.it]]
-    *  [[dino.pedreschi@unipi.it]]
-===== DM: Data Mining (9 CFU) =====
-Instructors:
-  * **Dino Pedreschi, Anna Monreale**
-    * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
-    * [[http://www-kdd.isti.cnr.it]]
-    * [[mirco.nanni@isti.cnr.it]]
-    * [[dino.pedreschi@unipi.it]]
-    * [[anna.monreale@unipi.it]]
-Teaching assistant - Assistente:
   * **Riccardo Guidotti**
-    * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa
+    * KDDLab, Università di Pisa
-    * [[guidotti@di.unipi.it]]
+    * [[https://kdd.isti.cnr.it/people/guidotti-riccardo]]
+    * [[riccardo.guidotti@di.unipi.it]]
-====== News =====
+Teaching Assistant
-  * **The project for DM2 is out!**
+  * **Andrea Fedele**
-  * **Results of DM2 mid-term exam are out: {{ :dm:results.2019.04.04.pdf |Results}}**
+    * KDDLab, Università di Pisa
-  * Last exam session on Feb, 14. Please register your name here: https://doodle.com/poll/6dgc5du4fgpnbyyx
+    * [[https://www.linkedin.com/in/andrea-fedele/?originalSubdomain=it]]
-  * Results of the written exam of Feb {{ :dm:dm_evaluation_1819_-_appello-feb.pdf |}}
+    * [[andrea.fedele@phd.unipi.it]]
-  * Results of the written exam of January {{:dm:dm_evaluation_1819-jan-session.pdf |}}
+    * Meeting: https://calendly.com/andreafedele/
-  * Dates for exam registration: (a) Jan 21: slot 14 - 15, 16-17; (b) Jan 22: slot 10 - 11; ( c ) Jan 23: slot 09 - 10. Location: Monreale's office.
+====== News ======
-  * ** I setup 3 days for the oral exam: 25, 28, 29 January. Other dates will we available after the written exam of Feb. For booking your oral exam please use the doodle indicating you Surname and Name: https://doodle.com/poll/3wunys9yd8s9q8ay **
+     * **[02.09.2024]** Lectures will start on Monday 30 September 2024 at 11.00 room C1.
-  * Final results including project evaluation available here: {{ :dm:dm_evaluation_1819.pdf |}}. If you do not find your evaluation please write an email to Anna Monreale.
+     * [02.09.2024] Lectures will be in presence only. Registrations of the lectures of past years can be found at the bottom of this web page.
-  * **New project is available!**
+     * [02.09.2024] Project Groups [[TODO|link]]
-  * *Results of the {{ :dm:secondmidterm-2018.pdf |Second mid-term test}}. After the evaluation of the project we will propose you the average grade considering: first and second midterm tests and project. *
+     * [11.09.2023] MS Teams [[TODO|link]]
-  * Get clusters from scipy dendogram: https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.fcluster.html#scipy.cluster.hierarchy.fcluster
+====== Learning Goals ======
-  * Help for installing Pyfim library https://anaconda.org/conda-forge/pyfim, https://pypi.org/project/fim/, http://www.borgelt.net/pyfim.html
+  * DM1
-  * *Results of the {{ :dm:20181030-midterm-test.pdf | first mid-term test}}.*
+     * Fundamental concepts of data knowledge and discovery.
-  * Students need to decide the group composition for the project and fill this [[https://docs.google.com/spreadsheets/d/1LubmSiJobg6WstjojdG4r7er2pzOrFrDElG6kdVJrAM/edit?usp=sharing| spreadsheet]] within October 1, 2018. It is strongly recommended an heterogenous composition with respect to the master degree. The number of members of each group can be 3 or 4.
+     * Data understanding
+     * Data preparation
+     * Clustering
+     * Classification
+     * Pattern Mining and Association Rules
+     * Sequential Pattern Mining
+  * DM2
+     * Outlier Detection
-====== Learning goals -- Obiettivi del corso ======
+     * Dimensionality Reduction
+     * Regression
+     * Advanced Classification and Regression
+     * Time Series Analysis
+     * Transactional Clustering
+     * Explainability
-** ... 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. **
+====== Hours and Rooms ======
-//Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.//
+===== DM1 =====
-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:
+**Classes**
-  - i concetti di base del processo di estrazione della conoscenza: studio e preparazione dei dati, forme dei dati, misure e similarità dei dati;
-  - le principali tecniche di datamining (regole associative, classificazione e clustering). Di queste tecniche si studieranno gli aspetti formali e implementativi;
-  - alcuni casi di studio nell’ambito del marketing e del supporto alla gestione clienti, del rilevamento di frodi e di studi epidemiologici.
-  - 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 {{:dm:economist--010.pdf|download}}
-  * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http://tech.fortune.cnn.com/2011/09/06/data-scientist-the-hot-new-gig-in-tech/|link]]
-  * Welcome to the yotta world. The Economist, Sept. 2011 {{:dm:economist-2012-dm.pdf|download}}
-  * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1|link]]
-  * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http://www.ilsole24ore.com/art/tecnologie/2012-09-21/futuro-scritto-data-155044.shtml?uuid=AbOQCOhG|link]]
-  * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{:dm:crossroadsxrds2012fall-dl.pdf|download}}
-  * Peter Sondergaard, Gartner, Says Big Data Creates Big Jobs. Oct 22, 2012: [[https://www.youtube.com/watch?v=mXLy3nkXQVM|YouTube video]]
-  * Towards Effective Decision-Making Through Data Visualization: Six World-Class Enterprises Show The Way. White paper at FusionCharts.com. [[http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf|download]]
-====== Hours - Orario e Aule ======
-===== DM1 & DM =====
-**Classes - Lezioni**
 ^  Day of Week  ^  Hour  ^  Room  ^
-|  Lunedì/Monday |  14:00 - 16:00  |  Aula C1  |
+|  Monday  |  11:00 - 13:00  |  C1   |
-|  Mercoledì/Wednesday |  14:00 - 16:00  |  Aula C1  |
+|  Tuesday  |  14:00 - 16:00  |  C1  |
-|  Venerdì/Friday |  11:00 - 13:00  |  Aula C1  |
 **Office hours - Ricevimento:**
-  * Prof. Pedreschi: Lunedì/Monday  h 14:00 - 16:00, Dipartimento di Informatica
+  * Prof. Pedreschi
-  * Prof. Monreale: by appointment, Room 374/DO, Dept. of Computer Science.
+      * TBD
-  * Dr. Guidotti: class-appointment (see calendar)
+      * Online
+  * Prof. Guidotti
+      * Wednesday 16:00 - 18:00 or Appointment by email
+      * Room 363 Dept. of Computer Science or MS Teams
 ===== DM 2 =====
-**Classes - Lezioni**
+**Classes**
-^  Day of week  ^  Hour  ^  Room  ^
+^  Day of Week  ^  Hour  ^  Room  ^
-| Thursday | 14 - 16  | A1 |
+|  Monday   |  09:00 - 11:00  |  C   |
-| Friday | 16 - 18 | C1 |
+|  Wednesday  |  11:00 - 13:00  |  C  |
-**Office hours - Ricevimento:**
+**Office Hours - Ricevimento:**
+  * Tuesday 15.00-17.00 or Appointment by email
+  * Room 363 Dept. of Computer Science or MS Teams
-  * Nanni : appointment by email, c/o ISTI-CNR
 ====== Learning Material -- Materiale didattico ======
@@ Linea 159: / Linea 97: @@
   * Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, 2006
     * [[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.
+    * I capitoli 3, 5, 7 sono disponibili sul sito del publisher. -- Chapters 3,5 and 7 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.
@@ Linea 165: / Linea 103: @@
-===== Slides of the classes -- Slides del corso =====
+===== Slides =====
-  * 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 [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]].
+  * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook's authors [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|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: [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php#item4|Slides per "Introduction to Data Mining"]]//
-===== Past Exams =====
-* Some text of past exams on **DM1 (6CFU)**:
+===== Software=====
-  * {{ :dm:2017-1-19.pdf |}}, {{ :dm:2017-9-6.pdf |}}, {{ :dm:2016-05-30-dm1-seconda.pdf |}}
+  * Python - Anaconda (>3.7): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/distribution/| Download page]] (the following libraries are already included)
+  * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | 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. [[http://pandas.pydata.org/ | Documentation page]]
-* Some solutions of past exams containing exercises on KNN and Naive Bayes classifiers  **DM1 (9CFU)**:
+Other softwares for Data Mining
-  * {{ :dm:dm2_exam.2017.06.13_solutions.pdf |}}, {{ :dm:dm2_exam.2017.07.04_solutions.pdf |}}, {{ :dm:dm2_mid-term_exam.2017.06.06_solutions.pdf |}}
+  * [[http://www.knime.org | KNIME ]] The Konstanz Information Miner. [[http://www.knime.org/download-desktop| Download page ]]
+  * [[http://www.cs.waikato.ac.nz/ml/weka/ | WEKA ]] Data Mining Software in JAVA. University of Waikato, New Zealand [[http://www.cs.waikato.ac.nz/ml/weka/ | Download page ]]
+  * Didactic Data Mining [[http://matlaspisa.isti.cnr.it:5055/Help| DDMv1]], [[https://kdd.isti.cnr.it/ddm/#/| DDMv2]]
+====== Class Calendar (2024/2025) ======
-* Some exercises (partially with solutions) on **sequential patterns** and **time series** can be found in the following texts of exams from the last years:
+===== First Semester (DM1 - Data Mining: Foundations) =====
-    * {{ :dm:dm2_exam.2015.04.13.results.pdf|}}, {{ :dm:dm2_exam.2016.04.4_sol.pdf |}}, {{ :dm:dm2_exam.2016.04.5_sol.pdf |}}, {{ :dm:dm2_exam.2016.06.20_sol.pdf |}}, {{ :dm:dm2_exam.2016.07.08_sol.pdf |}}
+^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^
+|   | 16.09.2023 | |  | No Lecture |  |  |
+|   | 17.09.2023 | |  | No Lecture |  |  |
+|   | 23.09.2023 | |  | No Lecture |  |  |
+|   | 24.09.2023 | |  | No Lecture |  |  |
+|01.| 30.09.2024 | 11-13 |C1| Overview, Introduction | {{ :dm:00_dm1_introduction_2023_24.pdf | Intro}} | Pedreschi|
-  * 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:
+===== Second Semester (DM2 - Data Mining: Advanced Topics and Applications) =====
-    * {{tdm:verifica2006.pdf|Verifica 2006}}, {{tdm:verifica2005.pdf|Verifica 2005 (con soluzioni)}}, {{tdm:verifica2004.pdf|Verifica 2004}}
-    * {{dm:verifica.05.06.2007.pdf|Verifica 5 giugno 2007}}, {{dm:verifica.26.06.2007.pdf|Verifica 26 giugno 2007}}, {{dm:verifica.24.07.2007_corretto.pdf|Verifica 24 luglio 2007}} (e {{dm:verifica.24.07.2007_soluzioni.pdf|Soluzioni}})
-    * {{:dm:verifica.2008.04.03.pdf|Verifica 3 aprile 2008}} (e {{:dm:soluzioni.2008.04.03.pdf|Soluzioni}}), {{:dm:dm-tdm.appello_2008_07_18_parte1.pdf|Verifica 18 luglio 2008 - parte 1}}, {{:dm:dm-tdm.appello_2008_07_18_parte2.pdf|Verifica 18 luglio 2008 - parte 2}}
-    * {{:dm:appello.2010.06.01_soluzioni.pdf| Exam with solution 2010-06-01}} {{:dm:appello.2010.06.22_soluzioni.pdf|Exam with solution 2010-06-22}} {{:dm:appello.2010.09.09_soluzioni.pdf|Exam with solution 2010-09-09}}{{:dm:appello.2010.07.13_soluzioni.pdf| Exam with solution 2010-07-13}}
-===== Data mining software=====
+^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^
+|01.| 19.02.2024 | 14-16 |C| Overview, Rule-based Models | {{ :dm:14_dm2_intro_2023_24.pdf | Introduction}}, {{ :dm:dm2_project_guidelines_23_24.pdf | Guidelines}}, {{ :dm:15_dm2_rule_based_classifier_2023_24.pdf | Rule-based Models }} | Guidotti|
-  * [[http://www.knime.org | KNIME ]] The Konstanz Information Miner. [[http://www.knime.org/download-desktop| Download page ]]
+====== Exams ======
-  * [[https://www.continuum.io/downloads | Python - Anaconda (2.7 version!!!)]]: Anaconda is the leading open data science platform powered by Python. [[https://www.continuum.io/downloads | Download page]] (the following libraries are already included)
-  * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | 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. [[http://pandas.pydata.org/ | Documentation page]]
-  * [[http://www.cs.waikato.ac.nz/ml/weka/ | WEKA ]] Data Mining Software in JAVA. University of Waikato, New Zealand [[http://www.cs.waikato.ac.nz/ml/weka/ | Download page ]]
-====== Class calendar - Calendario delle lezioni (2018/2019) ======
+** How and Where: **
+The exam will take place in oral mode only at the teacher's office or classroom previously designated.
+The exam will be held online on the 420AA Data Mining course channel only at the request of the
+student in accordance with current legislation.
-===== First part of course, first semester (DM1 - Data mining: foundations & DM - Data Mining) =====
+** When: **
+The dates relating to the start of the three exams are/will be published on the online platform
+https://esami.unipi.it/. Within each session, we will identify dates and slots in order to distribute the
+various orals. The dates and slots to take the exam will be published on the course page by the end of
+May. Each student must also register on https://esami.unipi.it/. The examination can only be carried out after the delivery of the project. The project must be delivered one week before when you want to take the exam. Group oral discussions will be preferred in respect of the project groups in order to parallelize any discussion on the project. It is not mandatory to take the oral exam together with the other members of the group.
+In the event that the oral exam is not passed, it will not be possible to take it for 20 days. If the project is not considered sufficient, it must be carried out again on a new dataset or a very updated version of the current one.
-^ ^ Day ^ Aula ^ Topic ^ Learning material ^ Instructor ^
+** What: **
-|1.|  19.09  14:00-16:00 | C1  | Overview. Introduction.        | {{ :dm:1.2018-dm-overview.pdf |}} | Pedreschi  |
+The oral test will evaluate the practical understanding of the algorithms. The exam will evaluate three aspects.
-|2.|  20.09  16:00-18:00 | C1  | Introduction                    |  | Pedreschi  |
+  - Understanding of the theoretical aspects of the topics addressed during the course. The student may be required to write on formulas or pseudocode. During the explanations, the student can use pen and paper.
-|  |  21.09  11:00-13:00 | C1  | Lecture canceled    |  | Pedreschi  |
+  - Understanding of the algorithms illustrated during the course and their practical implementation. You will be asked to perform one or more simple exercises. The text will be shown on the teacher's screen and / or copied to Miro. The student will have to use pen and paper (if online by Miro https://miro.com/ to show how the exercise is solved.
-|3.|  24.09  14:00-16:00 | C1  | KDD Process & Applications. Data Understanding.    | {{ :dm:2.2018-dm-introduction.pdf |DM + Applications}} {{ :dm:2-dataunderstanding-sa.pdf |DU}}| Monreale  |
+  - Discussion of the project with questions from the teacher regarding unclear aspects,
-|4.|  26.09  14:00-16:00 | C1  | Data Understanding. Data Preparation      |  | Monreale  |
+questionable steps or choices.
-|5.|  28.09  11:00-13:00 | C1  | Introduction to Python, Knime             | {{ :dm:00_start_with_knime.zip | intro_knime}} {{ :dm:intro_python_jupyter.zip | intro_python}} | Monreale/Guidotti  |
-|6.|  01.10  14:00-16:00 | C1  | Data Preparation  | {{ :dm:3.dm_ml_data_preparation.pdf | Data Preparation}} | Monreale |
-|7.|  03.10  14:00-16:00 | C1  | Clustering Introduction e Centroid-based clustering    | {{ :dm:4.basic_cluster_analysis-intro-kmeans.pdf |}} | Monreale |
-|  |  05.10  11:00-13:00 | C1  | Lecture canceled |  |  |
-|8.|  08.10  14:00-16:00 | C1  | Knime - Python: Data Understanding  |{{ :dm:01_data_understanding.zip | du_knime}} {{ :dm:titanic_data_understanding.ipynb.zip | du_python}}| Guidotti |
-|9.|  10.10  14:00-16:00 | C1  | Clustering: K-means & Hierarchical   | {{ :dm:5.basic_cluster_analysis-hierarchical.pdf |}}| Pedreschi |
-|  |  12.10  11:00-13:00 | C1  | Lecture canceled for IF |  | |
-|10.|  15.10  14:00-16:00 | C1  | Clustering: DBSCAN   | {{ :dm:6.basic_cluster_analysis-dbscan-validity.pdf |}}| Pedreschi |
-|11.|  17.10  14:00-16:00 | C1  | Clustering: Validity   | | Pedreschi |
-|12.|  19.10  11:00-13:00 | C1  | Discussion on Projects - DU |  | Guidotti |
-|13.|  22.10  14:00-16:00 | C1  | Exercises for mid-term test | Tool for Dm ex: [[http://matlaspisa.isti.cnr.it:5055/Help|Didactic Data Mining ]] {{ :dm:ex-clustering.pdf | Ex. Clustering PDF}} {{ :dm:ex-clustering.zip |Ex. Clustering PPTX}}| Monreale |
-|14.|  24.10  14:00-16:00 | C1  | Knime - Python: Clustering  | {{ :dm:knime_clustering.zip |clustering_knime}} {{ :dm:python_clustering.zip |clustering_python}}| Guidotti |
-|15.|  26.10  11:00-13:00 | C1  | Exercises for mid-term test | {{ :dm:clustering-2.zip |Ex. Clustering PPTX - complete }} {{ :dm:clustering-2.pdf |Ex. Clustering PDF - complete }} {{ :dm:exercises-dm1.pdf | Exercises DU}} {{ :dm:ex-silhouette.pdf |}}| Monreale |
-|16.|  05.11  14:00-16:00 | C1  | Classification/1  | {{ :dm:7.chap3_basic_classification.ppt |}}| Monreale |
-|17.|  07.11  14:00-16:00 | C1  | Classification/2   | | Monreale |
-|   |  09.11  11:00-13:00 | C1  | CANCELED| | |
-|18.|  12.11  14:00-16:00 | C1  | LAB: Classification  |{{ :dm:knime_classification.zip | knime_classification}} {{ :dm:python_classification.zip | python_classification}} | Guidotti |
-|19.|  14.11  14:00-16:00 | C1  | Pattern Mining   | {{ :dm:opentheblackbox.pdf | Explanation of classification/ML models }} {{ :dm:dm_patternmining.intro.pptx.pdf | Pattern mining Intro }}  {{ :dm:8.tdm-patterns-assrules.pdf | Apriori Algorithm for Pattern/AR Mining }}   | Pedreschi|
-|20.|  16.11  11:00-13:00 | C1  | Pattern Mining| | Pedreschi |
-|21.| 19.11 14:00-16:00	| C1| Exercises for the mid-term| {{ :dm:ex-second-midterm.pdf |}} |Monreale |
-|22.| 21.11 14:00-16:00	| C1| Lab Pattern Mining+ Discussion Clustering |{{ :dm:pattern_knime.zip |knime_pattern}} {{ :dm:pattern_python.zip |python_pattern}} https://anaconda.org/conda-forge/pyfim, https://pypi.org/project/fim/, http://www.borgelt.net/pyfim.html|Guidotti/Pedreschi|
-| | | | **The next lectures are dedicated to the DM of 9 credits** | | |
-|23.| 23.11 11:00-13:00	| C1| Alternative methods for Pattern Mining. Privacy in DM | {{ :dm:fp-growth.pdf |}}|Monreale|
-|24.| 26.11 14:00-16:00	| C1| Alternative methods for Clustering. Privacy in DM | {{ :dm:1-alternative-clustering.pdf |}}|Monreale|
-|25.| 28.11 14:00-16:00	| C1| Privacy in DM. Transactional Clustering | {{ :dm:2-transactionalclustering.pdf |}} {{ :dm:privacydt.pdf |}} {{ :dm:papers.zip |Papers on Clustering}} |Monreale|
-|26.| 30.11 11:00-13:00	| C1| Alternative methods for classification/1 | {{ :dm:lezioneadvancedclassificationmethods1-knn_nb.pdf | K-Nearest Neighbors & Naive Bayes }} |Pedreschi|
-|27.| 03.12 14:00-16:00	| C1| Alternative methods for classification/2  | {{ :dm:lezioneadvancedclassificationmethods3_rules-ensemble.pdf | Ensemble methods}}  {{ :dm:ensemblemethod_wisdomofthecrowd.pdf | Wisdom of the crowd & Ensemble methods }} {{ :dm:voxpopuli-galton-1907.pdf | Galton's Vox Populi}} |Pedreschi|
-|28.| 05.12 14:00-16:00	| C1| Alternative methods for classification/3  | |Pedreschi|
-|29.| 07.12 11:00-13:00	| C1| Exercises on clustering and classification | {{ :dm:exercises-clope.pdf | CLOPE}} {{ :dm:exercises-clustering-kmode.pdf | K-mode}} {{ :dm:ex-classification-knn-nb.pdf | KNN & NB}}|Monreale|
-|30.| 10.12 14:00-16:00	| C1| Exercises on Second part - all students | | Monreale|
-|31.| 12.12 14:00-16:00	| C1|Final Discussion on Project - all students  | |Pedreschi/Guidotti|
-|32.| 14.12 11:00-13:00	| C1| Cancelled | | |
+** Final Mark: ** for 12-credit exam, the final mark will be obtained as the
+average mark of DM1 and DM2.
- ===== Second part of course, second semester (DMA - Data mining: advanced topics and case studies) =====
+===== Exam Booking Periods =====
+  * Exam portal link: [[https://esami.unipi.it/|here]]
+  * 1st Appello: from TBD to TBD
+  * 2nd Appello: from TBD to TBD
+  * 3rd Appello: from TBD to TBD
+  * 4th Appello: from TBD to TBD
+  * 5th Appello: from TBD to TBD
+  * 6th Appello: from TBD to TBD
+===== Exam Booking Agenda =====
+When registering for the oral exam please specify in the notes DM1 if you do not want to do DM2 (that is assumed by default). After having booked for DM1 please contact Prof. Pedreschi to agree on the exam date (put Prof. Guidotti and Andrea Fedele in cc). There will be no agenda for DM1.
-^ ^ Day ^ Room (Aula) ^ Topic ^ Learning material ^ Instructor (default: Nanni)^
+  * 1st Appello - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|1.| 21.02.2019 14:00-16:00 | A1 | Introduction + Sequential patters/1 | {{ :dm:dm2_2019_intro.pdf |Introduction}}, {{ :dm:sequential_patterns_2019.pdf |Sequential patterns}} |  |
+  * 2nd Appello - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|2.| 22.02.2019 16:00-18:00 | C1 | Sequential patterns/2    |  |  |
+  * 3rd Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|3.| 01.03.2019 16:00-18:00 | C1 | Sequential patterns/3 | {{ :dm:exercises_2019.03.01_fixed.zip |Sample exercises (fixed)}} |  |
+  * 4th Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|4.| 07.03.2019 14:00-16:00 | A1 | Sequential patterns/4 | Sequential pattern tools: Link to [[http://www.philippe-fournier-viger.com/spmf/|SPMF]] + {{ :dm:spmf_datasets.zip | Sample datasets}}, {{ :dm:gsp_py_2019.zip |Python2 GSP educational implementation}}([[http://sequenceanalysis.github.io/|source]]), [[https://github.com/chuanconggao/PrefixSpan-py|PrefixSpan-py]] (requires Python3) |  |
+  * 5th Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|5.| 08.03.2019 16:00-18:00 | C1 | Time series/1    | {{ :dm:time_series_2019.pdf |Time series}} |  |
+  * 6th Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD)
-|6.| 14.03.2019 14:00-16:00 | A1 | Time series/2 | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf|Overview on DM for time series]], [[https://pdfs.semanticscholar.org/18f3/55d7ef4aa9f82bf5c00f84e46714efa5fd77.pdf|DTW paper by Sakoe and Chiba, 1978]] |  |
-|7.| 15.03.2019 16:00-18:00 | C1 | Time series/3 | |  |
-|8.| 21.03.2019 14:00-16:00 | A1 | Time series/4 | {{ :dm:timeseries_1_preprocess_2019.zip |Preprocessing in Python}} {{ :dm:timeseries_2_dtw_2019.zip |DTW in Python}} |  |
-|9.| 22.03.2019 16:00-18:00 | C1 | Time series/5 | |  |
-|10.| 28.03.2019 14:00-16:00 | A1 | Exercises for mid-term exam | {{ :dm:0.dm2_mid-term_exam.2018.04.10.pdf |Exercises from past exams}} |  |
-|11.| 29.03.2019 16:00-18:00 | C1 | Exercises for mid-term exam | {{ :dm:exercises_2019.03.29.zip |Exercises from past exams (with some solutions)}} |  |
-|   | 04.04.2019 16:00-18:00 | A1 + E | **mid-term exam** | | |
-|11.| 11.04.2019 14:00-16:00 | A1 | Classification: alternative methods/1  | {{ :dm:lezioneadvancedclassificationmethods1-knn_nb.pdf |kNN and Bayes classifier}}  |   |
-|12.| 12.04.2019 16:00-18:00 | C1 | Classification: alternative methods/2  | {{ :dm:classification_nnandsvm.pdf |NN and SVM}}, {{ :dm:exercises_classification_2.pdf |Exercises}}  |   |
-|  | <del>02.05.2019 14:00-16:00</del> | <del>A1</del> | Cancelled  |  |   |
-|13.| 03.05.2019 16:00-18:00 | C1 | Classification: alternative methods/3  |   |   |
-|14.| 09.05.2019 14:00-16:00 | A1 | Classification: alternative methods/4  | {{ :dm:neural_networks_svm_validation.pdf |Ex. on NNs and SVM}}, {{ :dm:dm2_exam.2018.07.03.pdf |Ex. on KNN and Naive Bayes}} |   |
-|15.| 10.05.2019 16:00-18:00 | C1 | Classification: alternative methods/5  |   |   |
-|16.| 16.05.2019 14:00-16:00 | A1 | Classification: Model Evaluation  | {{ :dm:2019.05.16_1_unbalanced_data_2019.pdf |Unbalanced data}}, {{ :dm:2019.05.16_3_classification_weights.pdf |Classification weights}} |   |
-|17.| 17.05.2019 16:00-18:00 | C1 | Classification: alternative methods/6  |  {{ :dm:ensemblemethod_wisdomofthecrowd.pdf |Ensembles}}, {{ :dm:2019.05.16_4_homeworks.pdf |Homeworks!}} |   |
-====== Exams ======
-===== Exam DM part I (DMF) ======
-The exam is composed of three parts:
+**Do not forget to make the evaluation of the course!!!**
+===== Exam DM1 ======
-  * 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.
+The exam is composed of two parts:
-  * An **oral exam (optional) **, 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. It is optional for students passing the written part by ONLY mid-term tests.
+  * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises.
-  * 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 min 3, 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 paper must emailed to [[datamining.unipi@gmail.com]]. Please, use “[DM 2018-2019] Project 2” in the subject. ** Students who will decide to perform the project during the winter exam sessions, or summer exam sessions will find the dataset of the project online after 31/05/2019. In this case the project must be delivered at least 2 days before the oral exam**.
+  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: data understanding, clustering analysis, pattern mining, and classification (guidelines will be provided for more details). The project has to be performed by min 2, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The paper must be emailed to [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM1 2023-2024] Project” in the subject.
-Tasks of the project:
-      - ** Data Understanding (Collective discussion on: 19/10/2018): ** 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. (see Guidelines for details)
+  * **Dataset**
-      - ** Clustering analysis (Collective discussion on: 21/11/2018): ** 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)
+    - Assigned: 30/09/2024
-      - ** Classification (Collective discussion on: 12/12/2018): ** Explore the dataset using classification trees and random forest. Use them to predict the target variable. (see Guidelines for details)
+    - MidTerm Submission: 15/11/2024 (+0.5) (half project required, i.e., Data Understanding & Preparation and Clustering)
-      -  ** Association Rules (Collective discussion on: 12/12/2018): ** 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 target variable. (see Guidelines for details)
+    - Final Submission: 31/12/2024 (+0.5) one week before the oral exam (complete project required).
+    - Dataset: TBD
+** DM1 Project Guidelines **
+See {{ :dm:dm1_project_guidelines_23_24.pdf | Project Guidelines}}.
-  * Project 1
-      - Dataset: **Credit Card Default**
-      - Assigned: 01/10/2018
-      - Deadline: <del>05/01/2019</del>, 09/01/2019
-      - Link: https://www.kaggle.com/t/5d7277746f8d45d6a10686506f602a9b
-  * Project 2
-      - Dataset: **Telco Customer Churn**
-      - Assigned: 10/01/2019
-      - Deadline: 31/05/2019
-      - Link: https://www.kaggle.com/blastchar/telco-customer-churn
+===== Exam DM2 ======
+The exam is composed of two parts:
+  * An **oral exam**, that includes: (1) discussing the project report; (2) discussing topics presented during the classes, including the theory and practical exercises.
- **Guidelines for the project are [[dm:start:guidelines|here]].**
+  * A **project**, that consists in exercises requiring the use of data mining tools for analysis of data. Exercises include: imbalanced learning, dimensionality reduction, outlier detection, advanced classification/regression methods, time series analysis/clustering/classification (guidelines will be provided for more details). The project has to be performed by min 1, max 3 people. It has to be performed by using Python or any other data mining software. The results of the different tasks must be reported in a unique paper. The total length of this paper must be max 30 pages of text including figures. The paper must be emailed to [[andrea.fedele@phd.unipi.it]] and [[riccardo.guidotti@unipi.it]]. Please, use “[DM2 2023-2024] Project” in the subject.
-===== Exam DM part II (DMA) ======
+  * **Dataset**
+    - Assigned: 19/02/2024
+    - MidTerm Submission: 07/05/2024 (Modules 1 and 2 (for TS classification non DL-based models))
+    - Final Submission: one week before the oral exam (complete project required, also with DL-based models for TS classification).
+    - Dataset: TBD
-The exam is composed of three parts:
+** DM2 Project Guidelines **
+See {{ :dm:dm2_project_guidelines_23_24.pdf | Project Guidelines}}.
-  * 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: [[https://thinfi.com/2etq|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.nanni@isti.cnr.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**, that 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.
-     * **Dataset**: the data is a time series dataset on air quality, which can be downloaded here: [[https://data.world/uci/air-quality|Dataset]].
-     * **Task 1: Time series**: Consider only attribute "PT08.S1(CO)" and split the corresponding time series into daily series, deleting those with too many missing values (value = -200) and fixing the others in some way. Make also sure that all time series have 24 values. Compute clustering (with an algorithm of your choice) based on DTW and Euclidean distances and compare the results.
-     * **Task 2: Sequential patterns**: discover contiguous sequential patterns of at least length 4. Before that, time series should be discretized in some way.
-     * **Task 3:Classification methods**: define a target variable "WE" for the time series data set to "true" for weekend days, and "false" for the others. Test the K-NN classification method using DTW as distance measure, and at least another classification method using the 24 values as separate variables.
-     * **Task 4: Outlier detection**: from the original dataset (i.e. the raw records with all attributes, not the time series built only on the "PT08.S1(CO)" attribute), identify the top 1% outliers. 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. Before doing the analysis, the records containing missing values should be deleted to avoid trivial results.
-====== Appelli di esame ======
+===== Past Exams =====
+  * Past exams texts can be found in old pages of the course. Please do not consider these exercises as a unique way of testing your knowledge. Exercises can be changed and updated every year and will be published together with the slides of the lectures.
-===== Mid-term exams =====
+===== Reading About the "Data Scientist" Job =====
-^ ^ Date ^ Hour ^ Place ^ Notes ^ Marks ^
+** ... 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. **
-| DM1: First Mid-term 2018 | 30.10.2018 | 11-13 | Room C1, L1, N1 | Please, use the system for registration: https://esami.unipi.it/| {{ :dm:20181030-midterm-test.pdf | results }} |
-| DM1: Second Mid-term 2018 | 18.12.2018| 11-13 | Room C1, L1, N1 | Please, use the system for registration: https://esami.unipi.it/| |
-| DM2: First Mid-term 2019 | 04.04.2019 | 16-18 | Room A1, E | Please, use the system for registration: https://esami.unipi.it/ \\ {{ :dm:dm2_mid-term_exam.2019.04.04_solutions.pdf |Solutions}}| {{ :dm:results.2019.04.04.pdf |Results}} |
-===== Appelli regolari / Exam sessions =====
+//Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.//
-^ Session ^ Date            ^ Time        ^ Room   ^ Notes ^ Marks ^
-|1.|16.01.2019| 14:00 - 18:00| Room E | | |
-|2.|06.02.2019| 14:00 - 18:00| Room E | | |
-===== Appelli straordinari A.A. 2017/18 / Extra sessions A.A. 20167/18=====
+  * Data, data everywhere. The Economist, Feb. 2010 {{:dm:economist--010.pdf|download}}
+  * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http://tech.fortune.cnn.com/2011/09/06/data-scientist-the-hot-new-gig-in-tech/|link]]
-^  Date            ^ Time        ^ Room   ^ Notes ^ Results ^
+  * Welcome to the yotta world. The Economist, Sept. 2011 {{:dm:economist-2012-dm.pdf|download}}
+  * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1|link]]
+  * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http://www.ilsole24ore.com/art/tecnologie/2012-09-21/futuro-scritto-data-155044.shtml?uuid=AbOQCOhG|link]]
+  * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{:dm:crossroadsxrds2012fall-dl.pdf|download}}
+  * Peter Sondergaard, Gartner, Says Big Data Creates Big Jobs. Oct 22, 2012: [[https://www.youtube.com/watch?v=mXLy3nkXQVM|YouTube video]]
+  * Towards Effective Decision-Making Through Data Visualization: Six World-Class Enterprises Show The Way. White paper at FusionCharts.com. [[http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf|download]]
 ====== Previous years =====
+  * [[dm_ds2023-24]]
+  * [[dm.2022-23ds]]
+  * [[dm.2021-22ds]]
+  * [[dm.2020-21]]
+  * [[dm.2019-20]]
+  * [[dm.2018-19]]
   * [[dm.2017-18]]
   * [[dm.2016-17]]
@@ Linea 341: / Linea 255: @@
   * [[dm.2012-13]]
   * [[dm.2011-12]]
-  * [[dm.2010-11]]
-  * [[dm.2009-10]]
-  * [[dm.2008-09]]
-  * [[dm.2007-08]]
-  * [[dm.2006-07]]
-  * [[PhDWorkshop2011]]
-  * [[SNA.Ingegneria2011]]
-  * [[SNA.IMT.2011]]
-  * [[MAINS.SANTANNA.2011-12]]
-  * [[MAINS.SANTANNA.DM4CRM.2012]]
-  * [[MAINS.SANTANNA.DM4CRM.2016]]
-  * [[MAINS.SANTANNA.DM4CRM.2017 | Data Mining for Customer Relationship Management 2017]]
-  * [[MAINS.SANTANNA.DM4CRM.2018]]
-  * [[MAINS.SANTANNA.DM4CRM.2019]]
-  * [[SDM2018 | Instructions for camera ready and copyright transfer]]
-  * [[DM-SAM | Storie dell'Altro Mondo]]