Icdm Topic Selection

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 for database-systems results.
• Route to an ML flagship (NeurIPS/ICML/ICLR) if the contribution is a general learning method with thin data-mining specificity.

Track fork within ICDM

If a project is a deployed system whose contribution is the deployment and its measured outcomes, the Applied Track fits and spares you the triple-blind anonymization burden. If the contribution is the algorithm and the deployment is illustrative, stay on Research.

Fit signal table

The routing calendar from ICDM's seat

ICDM's deadline sits in June, conference in November. That position matters when choosing where a finished project goes next: a paper not ready for ICDM's June can often target CIKM (spring deadline, autumn conference) the same year, WSDM (late-summer deadline, following spring), SDM (autumn deadline, following spring), or KDD's next cycle. Choose by community and format fit, not prestige — the same result reads differently to each pool.

Vignette: where a streaming anomaly detector goes

A project delivers a one-pass anomaly detector for edge streams with a memory bound and experiments on injected anomalies. ICDM reading: strong Research Track fit — a named mining mechanism, a scaling argument, and a discovery-validity claim. Strip the mechanism and keep only "we deployed it and fraud dropped," and it becomes an Applied Track paper (or a KDD applied submission). Grow it into a pure asymptotic analysis of the sketch with no system, and SDM becomes the better community.

Sharpening moves before committing

• Name the mining task and the data regime in one sentence; if you cannot, the ICDM framing does not exist yet.
• Name the single mechanism the contribution rests on, and the baseline it beats for a stated reason, not just on a leaderboard.
• Confirm the whole argument — body, references, appendix — can fit ICDM's 10-page all-inclusive cap; a result needing 14 pages is a journal or SDM paper.
• Topic emphasis and track lineup drift between editions; scan the current calls before final routing.

Output format

[Fit] strong ICDM / possible ICDM / better elsewhere
[Track] Research / Applied / Blue Sky
[Best venue] ICDM / KDD / SDM / CIKM / WSDM / WWW / ICDE / ML-flagship / journal
[Contribution sentence] <one sentence naming task + mechanism>
[Top rejection risk] <novelty / baselines / scale / discovery-validity / fit>
[Next action] <experiment, framing, track switch, or venue switch>