Notice bibliographique
Résumé
Online analytical processing (OLAP) and data mining are two important data analysis methods. To improve the functionality of OLAP systems, summary mining aims to find interesting summaries for a data set. In this thesis, we introduce a summary mining architecture, called GenSpace summary mining (GSSM), based on belief revision. Under this framework, a user's beliefs are represented by the user's estimated probability distribution (or estimates) for records in summaries. The summaries are organized in a graphical structure called a GenSpace graph. The GenSpace graph contains information about the conceptual levels of the summaries, the observed probability distributions of records in the summaries, and the user's estimates. The interestingness of the summaries is defined as the distance between the user's estimates and the observed probability distributions. During the mining process, the user specifies his/her estimates at a certain conceptual level in a GenSpace, and the system propagates them to other conceptual levels. The interesting summaries, i.e., the summaries far from the user's estimates, are then selected for presentation to the user. With the interesting summaries as new evidence, the user can revise his/her estimates and input them into the system to start the next round of the mining process. The GSSM process is iterative and it can be interactive. GenSpace summary mining consists of two parts, GenSpace estimate propagation (GSEP) and GenSpace summary selection (GSSS). The GSEP process is a probability based belief revision process. We argue that GSEP should preserve the consistency of the estimates in the GenSpace graph, be efficient, and guarantee the minimum change to the old estimates. Based on these principles, we formalize the GSEP problem as an optimization problem and propose a linear GSEP method as a heuristic approach to solving this problem. We then propose two pruning and path selection strategies to improve the propagation efficiency in GenSpace subgraphs. We also introduce virtual bottom nodes to further reduce the propagation and storage costs during the GSEP process. The experiments indicate that these techniques can greatly improve propagation efficiency. For the GSSS process, we study nine interestingness measures for summaries and their properties. These properties can be used as pruning strategies during the summary selection process to improve system efficiency. We demonstrate the effectiveness of the GSSM method on three real data sets, the Saskatchewan weather data set, the University of Regina student data set, and a customer data set. Experiments in all three data sets show that when the user accepts the distribution of the most interesting summary, its closely related summaries become less interesting in the following mining round and the user's estimates of all summaries approach the observed distributions.
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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».