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dm:mains.santanna.dm4crm.2017 [02/05/2017 alle 14:54 (7 anni fa)]
Anna Monreale [Calendar] |
dm:mains.santanna.dm4crm.2017 [23/05/2017 alle 15:30 (7 anni fa)] (versione attuale)
Anna Monreale [Exercises] |
| ====== Data Mining for Customer Relationship Management 2017 ====== | ====== Data Mining for Customer Relationship Management 2017 ====== |
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| * **Fosca Giannotti** ISTI-CNR, Knowledge Discovery and Data Mining Lab [[fosca.giannotti@isti.cnr.it]] | * **Fosca Giannotti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[fosca.giannotti@isti.cnr.it]] |
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| * **Dino Pedreschi** Università di Pisa, Knowledge Discovery and Data Mining Lab [[pedre@di.unipi.it]] | * **Dino Pedreschi**\\ Università di Pisa, Knowledge Discovery and Data Mining Lab\\ [[pedre@di.unipi.it]] |
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| * Assistente: **Riccardo Guidotti**, ISTI-CNR, Knowledge Discovery and Data Mining Lab [[riccardo.guidotti@di.unipi.it]] | * Teaching Assistant: **Riccardo Guidotti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[riccardo.guidotti@isti.cnr.it]] |
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| ===== News ===== | ===== News ===== |
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| ^ ^ Date ^ Topic ^ Learning material ^Instructor ^ | ^ ^ Date ^ Topic ^ Learning material ^Instructor ^ |
| |01. | 16.05.2017 - 09:00-13:00 | Introduction to data mining and big data analytics | {{:dm:1.dm_ml_introduction.pdf| slides: intro}} {{:dm:2.dm_ml-casestudies.ppt.pdf| slides: case studies}} | Giannotti | | |01. | 16.05.2017 - 09:00-13:00 | Introduction to data mining and big data analytics | {{:dm:1.dm_ml_introduction.pdf| slides: intro}} {{:dm:2.dm_ml-casestudies.ppt.pdf| slides: case studies}} | Pedreschi | |
| |02. | 16.05.2017 - 14:00-18:00 | Data understanding; data preparation; Knime tutorial | {{:dm:4.dm_ml_data_preparation.pdf| slides}} {{:dm:04_dataunderstanding.pdf| slides data understanding}} {{:dm:knime_slides_mains.pdf| Tutorial Knime}} {{ :dm:01_titanic_data_understanding.zip | 01_titanic_data_understanding}} | Pedreschi, Guidotti | | |02. | 16.05.2017 - 14:00-18:00 | Data understanding; data preparation; Knime tutorial | {{:dm:4.dm_ml_data_preparation.pdf| slides}} {{:dm:04_dataunderstanding.pdf| slides data understanding}} {{:dm:knime_slides_mains.pdf| Tutorial Knime}} {{ :dm:01_titanic_data_understanding.zip | 01_titanic_data_understanding}} | Pedreschi, Guidotti | |
| |03. | 17.05.2017 - 09:00-13:00 | Clustering analysis & customer segmentation | {{:dm:dm.pedreschi.clustering.2015.pdf| slides clustering}} {{:dm:customersegmentation.pdf| slides customer segmentation}} | Pedreschi | | |03. | 17.05.2017 - 09:00-13:00 | Clustering analysis & customer segmentation | {{:dm:dm.pedreschi.clustering.2015.pdf| slides clustering}} {{:dm:customersegmentation.pdf| slides customer segmentation}} | Pedreschi | |
| |05. | 18.05.2017 - 09:00-13:00 | Pattern and association rule mining & market basket analysis | {{:dm:3.dm-ml_patternmining.pdf|PatternMining-AR}} | Giannotti | | |05. | 18.05.2017 - 09:00-13:00 | Pattern and association rule mining & market basket analysis | {{:dm:3.dm-ml_patternmining.pdf|PatternMining-AR}} | Giannotti | |
| |06. | 18.05.2017 - 14:00-18:00 | Pattern and association rule mining: esercizi con Knime |{{ :dm:03_titanic_pattern.zip | 03_titanic_pattern}} {{ :dm:04_coop_pattern.zip | 04_coop_pattern}} | Giannotti, Guidotti | | |06. | 18.05.2017 - 14:00-18:00 | Pattern and association rule mining: esercizi con Knime |{{ :dm:03_titanic_pattern.zip | 03_titanic_pattern}} {{ :dm:04_coop_pattern.zip | 04_coop_pattern}} | Giannotti, Guidotti | |
| |07. | 19.05.2017 - 09:00-13}}:00 | Classification & prediction | {{:dm:dm.giannotti.pedreschi.classification.2015.pdf| slides classification}} [[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/|Visual Introduction to Classification with Decision Trees]] | Giannotti, Pedreschi, Guidotti | | |07. | 19.05.2017 - 09:00-13:00 | Classification & prediction | {{:dm:dm.giannotti.pedreschi.classification.2015.pdf| slides classification}} [[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/|Visual Introduction to Classification with Decision Trees]] | Giannotti, Pedreschi, Guidotti | |
| |08. | 19.05.2017 - 09:00-13:00 | Classification & prediction: esercizi con Knime | {{ :dm:05_titanic_classification.zip | 05_titanic_classification}} | Pedreschi, Guidotti | | |08. | 19.05.2017 - 14:00-18:00 | Classification & prediction: esercizi con Knime | {{ :dm:05_titanic_classification.zip | 05_titanic_classification}} | Pedreschi | |
| |09. | 22.05.2017 - 14:00-18:00 | Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-crm-redemption-churn-promozioni-profili-innovatori.pptx.pdf| slides}} {{:dm:crm_dm-survey.pdf|Survey of DM applications in CRM}} {{:dm:change-customer-behavior.pdf|Mining changes in customer behavior in retail marketing}} | Giannotti | | |09. | 22.05.2017 - 09:00-13:00 | Social network analysis: fundamentals | {{:dm:pedreschi_sna_crash_course_mains.pptx.pdf| slides}} | Pedreschi | |
| |10. | 22.05.2017 - 14:00-18:00 | Social network analysis: fundamentals | {{:dm:pedreschi_sna_crash_course_mains.pptx.pdf| slides}} | Pedreschi | | |10. | 22.05.2017 - 14:00-18:00 | Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-crm-redemption-churn-promozioni-profili-innovatori.pptx.pdf| slides}} {{:dm:crm_dm-survey.pdf|Survey of DM applications in CRM}} {{:dm:change-customer-behavior.pdf|Mining changes in customer behavior in retail marketing}} | Giannotti, Guidotti | |
| |11. | 23.05.2017 - 09:00-13:00 | Mobility data mining & big data analytics | | Giannotti | | |11. | 23.05.2017 - 09:00-13:00 | Mobility data mining & big data analytics | | Giannotti | |
| |12. | 23.05.2017 - 14:00-18:00 | Big Data Analytics: Privacy awareness | {{:dm:privacy-intro.pdf|Slides Privacy}}| Giannotti, Guidotti | | |12. | 23.05.2017 - 14:00-18:00 | Big Data Analytics: Privacy awareness | {{:dm:privacy-intro.pdf|Slides Privacy}} {{ :dm:06_class_mobility_mining.zip |}}| Giannotti, Guidotti | |
| ===== Datasets ===== | ===== Datasets ===== |
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| **Guidelines:** | **Guidelines:** |
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| Each group (2-3 people) is required to deliver a report (max 10 pages including all figures) describing the methods adopted and the discussion of the most interesting achieved results with reference to the tasks listed below. Assume that the report is targeted to a //marketing strategist//, who is interested to learn the story inferred in the various data mining analyses and to receive suggestions on how to take appropriate actions as a consequence. | Each group (2-3 people) is required to deliver a report (max 20 pages including all figures) describing the methods adopted and the discussion of the most interesting achieved results with reference to the tasks listed below. Assume that the report is targeted to a //marketing strategist//, who is interested to learn the story inferred in the various data mining analyses and to receive suggestions on how to take appropriate actions as a consequence. |
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| **1. Data Understanding**: useful as a preliminary step to capture basic data property. Distribution analysis, statistical exploration, correlation analysis, suitable transformation of variables and elimination of redundant variables, management of missing values. | **1. Data Understanding**: useful as a preliminary step to capture basic data property. Distribution analysis, statistical exploration, correlation analysis, suitable transformation of variables and elimination of redundant variables, management of missing values. |
| **4. Classification Analysis. ** Problem: find a high-quality decision tree for predicting a feature of a customer. The report should illustrate the adopted classification methodology and the decision tree validation and interpretation, describing also the process adopted to select the proposed tree, together with its quality evaluation. | **4. Classification Analysis. ** Problem: find a high-quality decision tree for predicting a feature of a customer. The report should illustrate the adopted classification methodology and the decision tree validation and interpretation, describing also the process adopted to select the proposed tree, together with its quality evaluation. |
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| **Deadline**: send the report by email to all instructors within **4 July 2017**. Specify [MAINS] in the subject of the email. | **Deadline**: send the report by email to all instructors within **23 June 2017**. Specify [MAINS] in the subject of the email. |
| ====== Exams ====== | ====== Exams ====== |
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