Individual skill
icdm-experiments
Use when designing or auditing the empirical evaluation for an ICDM (IEEE International Conference on Data Mining) paper - mining-task definition, strong and fairly-tuned baselines, ablations that isolate the named mechanism, scalability curves that test scale claims, and discovery-validity checks that separate real findings from evaluation artifacts.
ICDM-Skills/skills/icdm-experiments/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,411
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- testing Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 4,743 bytes
Read excerpt
# ICDM Experiments
Design the evaluation an ICDM reviewer will trust: a defined mining task, baselines tuned as
carefully as your method, ablations that isolate the mechanism, a measured scale story, and a
discovery-validity argument. ICDM's data-centric reviewers punish leaderboard-only wins and
un-checkable discovery claims, and the whole evaluation must fit inside the 10-page
all-inclusive cap.
## Define the mining task before the metric
- State the task operationally: inputs, outputs, and what a correct answer is. "Anomaly
detection" is a genre; "rank edges by anomalousness in a one-pass stream, evaluated against
injected ground truth" is a task.
- Fix the evaluation protocol — splits, negatives, thresholds, ranking cutoffs — before