Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Notice bibliographique
Résumé
The KDD 2002 conference, held from 23rd to 26th July 2002, was the eighth in the series. It represented a return to the country in which the series was launched: the first was held in Montreal, Canada, and this, the eighth, was held in Edmonton, Canada. In the years between the first conference in the series and this present one, data mining has be, come a well-established discipline. It has continued to strengthen its links to other data analytic disciplines, including statistics, machine learning, pattern recognition, visualization, and database technology, but has now clearly carved out a niche of its own. Over the period in which this series has been running, hardware technology has continued to advance in great leaps, with the result that large databases have continued to grow in both number and size. The implication is that the challenge of data mining is even more important, that the problems requiring data mining solutions are ever more ubiquitous, and that new tools and methods for tackling are even more necessary.KDD 2002 received a record number of submitted papers - 307 in total, 37 of which were considered for the industral/applicafion track. Among the 270 research submissions, 32 were selected (12%) for full papers; and among the 37 industrial/application submissions, 12 (32%) were selected for full papers. An additional 44 submissions were chosen to be presented as posters, a vast majority of which were research submissions. This low rate of acceptance reflects a conscious effort to maintain the very high standards of quality and relevance, which have been achieved by previous conferences in the series. It means that the papers and posters in the proceedings represent the cutting edge of data mining problemsl solutions, and technology. On the other hand, this policy inevitably meant that many excellent contributions did not make it to the final program. The choice had to be informed by balance as well as quality - KDD 2002 had to showcase research in data mining across the entire frontier of the discipline. This breadth was reflected in the choice of invited speakers, both well known in the data mining; community, but from different backgrounds: Daryl Pregibon and Geoff Hinton. The program also includes 6 workshops in such diverse areas as 'Data Mining in Bioinformatics', 'Web Mining', 'Multimedia Data Mining', 'Multi-Relational Data Mining', 'Temporal Data Mining', and 'Fractals in Data Mining' as well as 6 tutorials on 'Text Mining for Bioinformatics', 'Querying and Mining Data Streams', 'Link Analysis', 'Multivariate Density Estimation', 'Common Reasons Data Mining Projects Fail', and 'Visual Data Mining'.
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| 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 découle