icdm-topic-selection
Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ICDM Topic Selection Use this before writing. Two decisions happen here: is the work **ICDM-shaped at all**, and if so, **which track**. ICDM is the IEEE-sponsored data-mining flagship; it rewards a named data-mining mechanism on a defined mining task with strong baselines and a scalability or discovery-validity story — not pure learning theory, and not a broad deep-learning systems result. ## Fit test - Prefer ICDM when the contribution is a **data-mining method**: pattern discovery, graph mining, anomaly detection, temporal/streaming mining, clustering, or scalable analytics, with an algorithmic idea and defensible empirical evidence. - Route to **SDM (SIAM)** if the contribution is primarily mathematical/statistical rigor in a mining method — SDM's community weights theory and analysis more heavily. - Route to **KDD** if the work is large-scale applied discovery or a deployed data-science system aimed at the biggest data-mining audience and its two-cycle calendar. - Route to **CIKM** for information/knowledge management, IR, and database-adjacent work; to **WSDM** for web-and-social search and mining; to **WWW/TheWebConf** for web-native contributions; to **ICDE/SIGMOD/VLDB**
- Fit test
- Track fork within ICDM
- Fit signal table
- The routing calendar from ICDM's seat
- Vignette: where a streaming anomaly detector goes
- Sharpening moves before committing
- Output format
What does the icdm-topic-selection skill do?
Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.