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Introduction au datamining, concepts et techniques

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Face à l’importance grandissante que prend l’information, le datamining offre une solution pour maitriser la complexité et synthétiser les amas de données pour faire jaillir de la connaissance.

Cet exposé introduit la notion de datamining dans son ensemble à travers ses concepts et ses principes, et survole quelques techniques utilisées lors de ce processus.

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Introduction au datamining, concepts et techniques

  1. 1. Introduction au<br />Datamining<br />Concepts et techniques<br />BOUSSAIDI Abdellah<br />CHAÏB Ismaïl<br />ESI, 06/04/2009<br />
  2. 2. ?<br />?<br />?<br />?<br />?<br />connaissances<br />Montagne de données<br />Problème?<br />
  3. 3. Datamining<br />
  4. 4. Quoi Comment Jusqu’ou<br />?<br />
  5. 5. Quoi?<br />Le datamining est un processus de découvertede connaissances<br />connaissances<br />Datamining <br />Bases de données et Datawarehouses<br />
  6. 6. Pourquoi?<br />Description<br />Prédiction<br />
  7. 7. Exemples d’application<br />Segmentation des clients <br />Déterminer le panier de la ménagère <br />Détection de Fraude<br />
  8. 8. Champs d’application<br />
  9. 9. Avantages<br />Renforcer la position<br />compétitive de l’entreprise <br />Identifier les facteurs qui déterminent le comportement du client ;<br />Transformer des masses de données en information utile<br />Meilleure <br />prise de décision <br />Identifier les investissements les plus profitables et les moins couteux.<br />
  10. 10. QuoiCommentJusqu’ou<br />?<br />
  11. 11. Le datamining est un processus itératif<br />
  12. 12. Application du processus de datamining :Les Télécoms<br />
  13. 13. Formaliser le problème<br />Problématique:<br /><ul><li> Segmentation des clients
  14. 14. Taux de turn-over</li></li></ul><li>Collecte de données<br /><ul><li> Informations clients</li></ul> (nom, prénom, <br /> âge, profession..etc.);<br /><ul><li> Durée d’appel par client;
  15. 15. Heures d’appels;
  16. 16. Appels non aboutis;
  17. 17. …etc.</li></li></ul><li>Prétraitement des données<br /><ul><li> Atténuer le bruit </li></ul>Enlever les informations non pertinentes<br /><ul><li> Sélectionner les données utiles</li></ul>Mieux vaut tout prendre !<br /><ul><li> ACP…etc.</li></li></ul><li>16<br />Estimer le modèle<br />Sélectionner le modèle adéquat<br />SVM<br />régression<br />Logique<br />Floue<br />Méthodes<br />Statistique<br />Réseaux<br />Bayésiens<br />Meta<br />heuristique<br />Arbres<br />De décision<br />Réseaux <br />De Neurones<br />Google Confidential<br />
  18. 18. 17<br />Estimer le modèle<br /><ul><li> Implémenter la technique approprié </li></ul>SVM<br />Validation<br />Logique<br />Floue<br />Vérification<br />Réseaux<br />Bayésiens<br />Réechan<br />Réseaux <br />De Neurones<br />Méthodes<br />D’apprentissage<br />Google Confidential<br />
  19. 19. 18<br />Estimer le modèle<br />Estimation<br />De l’erreur<br />logiciel<br />Implémentation<br />Techniques<br />De datamining<br />Google Confidential<br />
  20. 20. 19<br />Interpréter le modèle <br />et tirer les conclusions<br />Résultats<br />Google Confidential<br />
  21. 21. Interpréter le modèle et tirer les conclusions<br />
  22. 22. Quoi CommentJusqu’ou<br />?<br />Le textmining<br />
  23. 23. Le TextMining<br />TextMining<br />Process<br />
  24. 24. Pourquoi?<br />80%<br />de l’information enregistrée, <br />l’est sous forme textuelle<br />
  25. 25. Quoi CommentJusqu’ou<br />?<br />
  26. 26. Logiciels de datamining<br />Poids lourds<br />SAS<br />SPSS Clementine<br />Weka (Open-source)<br />Tendance : datamining dans la base de données<br />Oracle Darwin Suite<br />SQL Server Analysis Services<br />
  27. 27. Limites du datamining<br />Effort considérable de développement.<br />Etat inapproprié des données.<br />
  28. 28. Perspectives<br />Analyse de plus de données dans un minimum de temps<br />Exploration plus approfondie des données<br />Datamining pour les PMEs.<br />Datamining « domestique »<br />
  29. 29. Conclusion<br />Datamining<br />
  30. 30. Merci pour votre attention<br />

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