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dm:start [03/10/2019 alle 19:32 (5 anni fa)] – [News] Anna Monreale | dm:start [16/09/2024 alle 06:41 (6 giorni fa)] (versione attuale) – Riccardo Guidotti | ||
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- | < | + | ====== Data Mining A.A. 2024/25 ====== |
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- | ga(' | + | ===== DM1 - Data Mining: Foundations |
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- | ====== Data Mining A.A. 2019/20 ====== | + | |
- | ===== DM 1: Foundations of Data Mining (6 CFU) ===== | + | Instructors: |
- | + | ||
- | Instructors | + | |
* **Dino Pedreschi** | * **Dino Pedreschi** | ||
- | * KDD Laboratory, Università di Pisa ed ISTI - CNR, Pisa | + | * KDDLab, Università di Pisa |
* [[http:// | * [[http:// | ||
* [[dino.pedreschi@unipi.it]] | * [[dino.pedreschi@unipi.it]] | ||
- | + | * **Riccardo Guidotti** | |
- | | + | |
+ | * [[https:// | ||
+ | * [[riccardo.guidotti@di.unipi.it]] | ||
- | ===== DM 2: Advanced topics on Data Mining and case studies | + | Teaching Assistant |
+ | * **Andrea Fedele** | ||
+ | * KDDLab, Università di Pisa | ||
+ | * [[https:// | ||
+ | * [[andrea.fedele@phd.unipi.it]] | ||
+ | ===== DM2 - Data Mining: Advanced Topics | ||
Instructors: | Instructors: | ||
- | * **Mirco Nanni, Dino Pedreschi** | + | * **Riccardo Guidotti** |
- | * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa | + | * KDDLab, Università di Pisa |
- | * [[http://www-kdd.isti.cnr.it]] | + | * [[https:// |
- | * [[mirco.nanni@isti.cnr.it]] | + | * [[riccardo.guidotti@di.unipi.it]] |
- | * [[dino.pedreschi@unipi.it]] | + | |
- | ===== DM: Data Mining (9 CFU) ===== | + | Teaching Assistant |
+ | * **Andrea Fedele** | ||
+ | * KDDLab, Università di Pisa | ||
+ | * [[https:// | ||
+ | * [[andrea.fedele@phd.unipi.it]] | ||
+ | * Meeting: https:// | ||
+ | ====== | ||
+ | * **[02.09.2024]** Lectures will start on Monday 30 September 2024 at 11.00 room C1. | ||
+ | * [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. | ||
+ | * [02.09.2024] Project Groups [[TODO|link]] | ||
+ | * [11.09.2023] MS Teams [[TODO|link]] | ||
+ | ====== Learning Goals ====== | ||
+ | * DM1 | ||
+ | * Fundamental concepts of data knowledge and discovery. | ||
+ | * Data understanding | ||
+ | * Data preparation | ||
+ | * Clustering | ||
+ | * Classification | ||
+ | * Pattern Mining and Association Rules | ||
+ | * Sequential Pattern Mining | ||
- | Instructors: | + | * DM2 |
- | * **Dino Pedreschi, Anna Monreale** | + | |
- | * KDD Laboratory, Università di Pisa and ISTI - CNR, Pisa | + | * Dimensionality Reduction |
- | * [[http:// | + | * Regression |
- | * [[mirco.nanni@isti.cnr.it]] | + | |
- | * [[dino.pedreschi@unipi.it]] | + | |
- | * [[anna.monreale@unipi.it]] | + | |
+ | | ||
+ | ====== Hours and Rooms ====== | ||
- | ====== News ===== | + | ===== DM1 ===== |
- | * **[03.10.2019] Please, fill the [[https:// | + | |
- | * [26.09.2019] Global Climate Strike: teachers of DM course tomorrow Friday September 27 will join the Global Climate strike, so tomorrow the lecture is suppressed. | + | |
- | * [18.09.2019] Event: " | + | |
- | + | ||
- | ====== 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/ | + | **Classes** |
- | + | ||
- | //Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.// | + | |
- | + | ||
- | La grande disponibilità di dati provenienti da database relazionali, | + | |
- | - 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, | + | |
- | - 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" | + | |
- | + | ||
- | * Data, data everywhere. The Economist, Feb. 2010 {{: | + | |
- | * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http:// | + | |
- | * Welcome to the yotta world. The Economist, Sept. 2011 {{: | + | |
- | * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http:// | + | |
- | * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http:// | + | |
- | * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{: | + | |
- | * Peter Sondergaard, | + | |
- | + | ||
- | * Towards Effective Decision-Making Through Data Visualization: | + | |
- | ====== Hours - Orario e Aule ====== | + | |
- | + | ||
- | ===== DM1 & DM ===== | + | |
- | + | ||
- | **Classes | + | |
^ Day of Week ^ Hour ^ Room ^ | ^ Day of Week ^ Hour ^ Room ^ | ||
- | | | + | | Monday |
- | | | + | | |
- | | Venerdì/ | + | |
**Office hours - Ricevimento: | **Office hours - Ricevimento: | ||
- | * Prof. Pedreschi: Lunedì/ | + | * Prof. Pedreschi |
- | * Prof. Monreale: | + | * TBD |
+ | * Online | ||
+ | * Prof. Guidotti | ||
+ | * Wednesday 16:00 - 18:00 or Appointment by email | ||
+ | * Room 363 Dept. of Computer Science or MS Teams | ||
| | ||
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- | **Classes | + | **Classes** |
- | ^ Day of week | + | ^ Day of Week |
- | | Thursday | + | | |
- | | Friday | + | | |
- | **Office | + | **Office |
+ | |||
+ | * 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 ====== | ====== Learning Material -- Materiale didattico ====== | ||
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* Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, | * Pang-Ning Tan, Michael Steinbach, Vipin Kumar. **Introduction to Data Mining**. Addison Wesley, ISBN 0-321-32136-7, | ||
* [[http:// | * [[http:// | ||
- | * I capitoli | + | * I capitoli |
* 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 | * 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. | * Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition. | ||
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- | ===== Slides | + | ===== 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' | + | * 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' |
- | ===== 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/ |
+ | * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/ | ||
+ | * Pandas: pandas is an open source, BSD-licensed library providing high-performance, | ||
- | * Some solutions of past exams containing exercises on KNN and Naive Bayes classifiers | + | Other softwares for Data Mining |
- | * {{ :dm:dm2_exam.2017.06.13_solutions.pdf |}}, {{ :dm:dm2_exam.2017.07.04_solutions.pdf |}}, {{ :dm: | + | |
+ | * [[http://www.cs.waikato.ac.nz/ | ||
+ | * Didactic Data Mining [[http:// | ||
+ | |||
+ | ====== Class Calendar (2024/2025) ====== | ||
- | * Some exercises | + | ===== First Semester |
- | * {{ : | + | |
+ | ^ ^ 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 | {{ : | ||
- | * Some very old exercises | + | ===== Second Semester |
- | * {{tdm: | + | |
- | * {{dm: | + | |
- | * {{: | + | |
- | * {{: | + | |
- | ===== Data mining software===== | + | ^ ^ Day ^ Time ^ Room ^ Topic ^ Material ^ Lecturer ^ |
+ | |01.| 19.02.2024 | 14-16 |C| Overview, Rule-based Models | {{ : | ||
- | | + | ====== Exams ====== |
- | * Python - Anaconda (3.7 version!!!): | + | |
- | * Scikit-learn: | + | ** How and Where: ** |
- | * Pandas: pandas | + | The exam will take place in oral mode only at the teacher' |
- | | + | 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. | ||
+ | |||
+ | ** 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 | ||
+ | In the event that the oral exam is not passed, it will not be possible | ||
+ | |||
+ | ** What: ** | ||
+ | The oral test will evaluate the practical understanding of the algorithms. The exam will evaluate three aspects. | ||
+ | | ||
+ | - 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' | ||
+ | - Discussion | ||
+ | questionable steps or choices. | ||
+ | |||
+ | ** Final Mark: ** for 12-credit exam, the final mark will be obtained as the | ||
+ | average mark of DM1 and DM2. | ||
+ | |||
+ | ===== 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 | ||
- | ====== Class calendar - Calendario delle lezioni (2019/2020) ====== | + | ===== 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. | ||
- | ===== First part of course, first semester | + | * 1st Appello - DM1 & DM2: from TBD to TBD (deliver project by TBD) |
+ | * 2nd Appello - DM1 & DM2: from TBD to TBD (deliver project by TBD) | ||
+ | * 3rd Appello: | ||
+ | * 4th Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD) | ||
+ | * 5th Appello: | ||
+ | * 6th Appello: - DM1 & DM2: from TBD to TBD (deliver project by TBD) | ||
- | ^ ^ Day ^ Topic ^ Learning material ^ Instructor ^ | ||
- | |1.| 16.09 14:00-16:00 | Overview. Introduction to KDD | {{ : | ||
- | | | 18.09 16:00-18:00 | Lecture canceled | ||
- | |2.| 20.09 11:00-13:00 | Introduction to KDD: technologies, | ||
- | |3.| 23.09 14:00-16:00 | Data Understanding (from Bertold book!) | ||
- | |4.| 25.09 16:00-18:00 | Data Preparation | ||
- | | | 27.09 11:00-13:00 | Climate Strike | ||
- | |5.| 30.09 14:00-16:00 | Introduction to Python. | ||
- | |6.| 02.10 16:00-18:00 | Clustering: Introduction + Centroid-based clustering, K-means | {{ : | ||
- | |7.| 04.10 11:00-13:00 | Lab: Data Understanding & Preparation in Python & Knime | Data: {{ : | ||
- | ===== Second part of course, second semester (DMA - Data mining: advanced topics and case studies) ===== | ||
- | ^ ^ Day ^ Room (Aula) ^ Topic ^ Learning material ^ Instructor (default: Nanni)^ | + | **Do not forget |
- | |1.| 21.02.2019 14:00-16:00 | A1 | Introduction + Sequential patters/1 | {{ : | + | ===== Exam DM1 ====== |
- | |2.| 22.02.2019 16:00-18:00 | C1 | Sequential patterns/ | + | |
- | |3.| 01.03.2019 16:00-18:00 | C1 | Sequential patterns/3 | {{ : | + | |
- | |4.| 07.03.2019 14:00-16:00 | A1 | Sequential patterns/4 | Sequential pattern tools: Link to [[http:// | + | |
- | |5.| 08.03.2019 16:00-18:00 | C1 | Time series/ | + | |
- | |6.| 14.03.2019 14:00-16:00 | A1 | Time series/2 | [[https:// | + | |
- | |7.| 15.03.2019 16:00-18:00 | C1 | Time series/3 | | | | + | |
- | |8.| 21.03.2019 14:00-16:00 | A1 | Time series/4 | {{ : | + | |
- | |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 | {{ : | + | |
- | |11.| 29.03.2019 16:00-18:00 | C1 | Exercises for mid-term exam | {{ : | + | |
- | | | 04.04.2019 16:00-18:00 | A1 + E | **mid-term exam** | | | | + | |
- | |11.| 11.04.2019 14:00-16:00 | A1 | Classification: | + | |
- | |12.| 12.04.2019 16:00-18:00 | C1 | Classification: | + | |
- | | | < | + | |
- | |13.| 03.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |14.| 09.05.2019 14:00-16:00 | A1 | Classification: | + | |
- | |15.| 10.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |16.| 16.05.2019 14:00-16:00 | A1 | Classification: | + | |
- | |17.| 17.05.2019 16:00-18:00 | C1 | Classification: | + | |
- | |18.| 23.05.2019 14:00-16:00 | A1 | Exercises + Outlier detection/ | + | |
- | |19.| 24.05.2019 16:00-18:00 | C1 | Outlier detection/ | + | |
- | |< | + | |
- | | | 06.06.2019 16:00-18:00 | E (+A1) | **mid-term exam** | {{ : | + | |
- | ====== Exams ====== | + | |
- | ===== Exam DM part I (DMF) ====== | + | The exam is composed of two parts: |
- | The exam is composed of three parts: | + | * An **oral |
- | * A **written exam**, with exercises | + | * A **project**, that consists in exercises |
+ | |||
+ | * **Dataset** | ||
+ | - Assigned: 30/ | ||
+ | - MidTerm Submission: 15/11/2024 (+0.5) (half project required, i.e., Data Understanding & Preparation | ||
+ | - 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}}. | ||
- | * A **project** consists in exercises that require the use of data mining tools for analysis of data. Exercises include: data understanding, | ||
- | Tasks of the project: | ||
- | - ** Data Understanding (Collective discussion on: 19/ | ||
- | - ** Clustering analysis (Collective discussion on: 21/ | ||
- | - ** Classification (Collective discussion on: 12/ | ||
- | - ** Association Rules (Collective discussion on: 12/ | ||
- | * Project 1 | ||
- | - Dataset: **Credit Card Default** | ||
- | - Assigned: 01/10/2018 | ||
- | - Deadline: < | ||
- | - Link: https:// | ||
+ | |||
+ | ===== Exam DM2 ====== | ||
- | * Project 2 | + | The exam is composed of two parts: |
- | - Dataset: **Telco Customer Churn** | + | |
- | - Assigned for the summer session. | + | |
- | - Deadline: 2 days before the oral exam. | + | |
- | - Link: https:// | + | |
+ | * 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 | + | |
- | ===== Exam DM part II (DMA) ====== | + | * **Dataset** |
+ | - Assigned: 19/ | ||
+ | - MidTerm Submission: 07/ | ||
+ | - 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: | ||
- | * 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; | ||
- | * A **project**, | ||
- | * **Dataset**: | ||
- | * **Task 1: Time series**: Consider only attribute " | ||
- | * **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: | ||
- | * **Task 4: Outlier detection**: | ||
- | ====== 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" |
- | ^ ^ 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/ |
- | | DM1: First Mid-term 2018 | 30.10.2018 | 11-13 | Room C1, L1, N1 | Please, use the system for registration: | + | |
- | | DM1: Second Mid-term 2018 | 18.12.2018| 11-13 | Room C1, L1, N1 | Please, use the system for registration: | + | |
- | | DM2: First Mid-term 2019 | 04.04.2019 | 16-18 | Room A1, E | Please, use the system for registration: | + | |
- | | DM2: Second Mid-term 2019 | 06.06.2019 | 16-18 | Room E \\ (+ A1 if needed) | Please, use the system for registration: | + | |
- | ===== Appelli regolari / Exam sessions ===== | + | |
- | ^ 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 | | | | + | |
- | |3.|19.06.2019| 09:00 - 13:00| Room A1 | Oral Exam on DM1 within 15 July. If you cannot do within that date you can do the oral exam on September.| {{ : | + | |
- | |4.|10.07.2019| 09:00 - 13:00| Room A1 |Oral Exam on DM1 within 15 July. If you cannot do within that date you can do the oral exam on September. | {{ : | + | |
- | ===== Appelli straordinari A.A. 2017/18 / Extra sessions A.A. 20167/ | + | |
- | ^ Date ^ Time ^ Room ^ Notes ^ Results ^ | + | //Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.// |
+ | |||
+ | * Data, data everywhere. The Economist, Feb. 2010 {{: | ||
+ | * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http:// | ||
+ | * Welcome to the yotta world. The Economist, Sept. 2011 {{: | ||
+ | * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http:// | ||
+ | * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http:// | ||
+ | * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{: | ||
+ | * Peter Sondergaard, | ||
+ | * Towards Effective Decision-Making Through Data Visualization: | ||
====== Previous years ===== | ====== Previous years ===== | ||
- | * [[dm.2018-19]] | + | * [[dm_ds2023-24]] |
- | * | + | * [[dm.2022-23ds]] |
+ | * [[dm.2021-22ds]] | ||
+ | * [[dm.2020-21]] | ||
+ | * [[dm.2019-20]] | ||
+ | | ||
+ | | ||
* [[dm.2016-17]] | * [[dm.2016-17]] | ||
* [[dm.2015-16]] | * [[dm.2015-16]] | ||
Linea 295: | Linea 255: | ||
* [[dm.2012-13]] | * [[dm.2012-13]] | ||
* [[dm.2011-12]] | * [[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' | + |
dm/start.1570131167.txt.gz · Ultima modifica: 03/10/2019 alle 19:32 (5 anni fa) da Anna Monreale