Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it