Visual Data Mining from Visualization to Visual Information Mining
Notice bibliographique
Résumé
The current explosion of data and information, which are mainly caused by the continuous adoption of data warehouses and the extensive use of the Internet and its related technologies, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, addresses this need. Data mining automates the discovery of hidden patterns and relationships that may not always be obvious. Data mining tools include classification techniques (such as decision trees, rule induction programs and neural networks) (Kou et al., 2007); clustering algorithms and association rule approaches, amongst others. Data mining has been fruitfully used in many of domains, including marketing, medicine, finance, engineering and bioinformatics. There still are, however, a number of factors that militate against the widespread adoption and use of this new technology. This is mainly due to the fact that the results of many data mining techniques are often difficult to understand. For example, the results of a data mining effort producing 300 pages of rules will be difficult to analyze. The visual representation of the knowledge embedded in such rules will help to heighten the comprehensibility of the results. The visualization of the data itself, as well as the data mining process should go a long way towards increasing the user’s understanding of and faith in the data mining process. That is, data and information visualization provide users with the ability to obtain new insights into the knowledge, as discovered from large repositories. This paper describes a number of important visual data mining issues and introduces techniques employed to improve the understandability of the results of data mining. Firstly, the visualization of data prior to, and during, data mining is addressed. Through data visualization, the quality of the data can be assessed throughout the knowledge discovery process, which includes data preprocessing, data mining and reporting. We also discuss information visualization, i.e. how the knowledge, as discovered by a data mining tool, may be visualized throughout the data mining process. This aspect includes visualization of the results of data mining as well as the learning process. In addition, the paper shows how virtual reality and collaborative virtual environments may be used to obtain an immersive perspective of the data and the data mining process as well as how visual data mining can be used to directly mine functionality with specific applications in the emerging field of proteomics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».