SIAM International Conference on Data Mining (SDM)
Conference positioning
SIAM International Conference on Data Mining (SDM) is a top computer-science conference venue for data mining with mathematical, statistical, optimization, graph, and scientific-computing depth. It rewards a data mining paper that benefits from SIAM-style rigor rather than product-style analytics framing. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
When to trigger
The author names SDM / SIAM International Conference on Data Mining as the target venue.
A manuscript in data mining with mathematical needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.
Scope & topic fit
Core fit: data mining with mathematical, statistical, optimization, graph, and scientific-computing depth.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to SDM's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.
Venue-specific calibration
Reviewer lens: Read reviewers as data-centric ML and discovery specialists. Novelty should appear in mining method, scale, discovery validity, or applied impact.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: data mining with mathematical, statistical, optimization, graph, and scientific-computing depth.
Official anchor domain: www.siam.org. Quote annual rules only after opening that source and the current-year CFP/author kit.
Close-neighbor routing guardrail
Route to SDM when the contribution emphasizes mathematical, statistical, optimization, or
principled data-mining methodology.
Do not treat SDM as a generic data-mining fallback. Compare KDD for impact and applied
discovery, ICDM for broad data-mining methods, and ML venues for learning-method
contributions.
What distinguishes this venue from its closest siblings
Sponsorship.SIAM-sponsored data-mining conference; sibling to IEEE ICDM and ACM KDD.
Center of gravity. SIAM venues lean methodological/algorithmic with an applied-math framing; route theory-forward mining methods here and large-scale applied/industrial work to KDD.
Routing. Scope overlaps ICDM/KDD heavily — differentiate by community and cycle, not prestige.
SDM-specific routing detail
Prefer SDM when the data-mining contribution has strong mathematical, statistical, optimization, graph, or algorithmic depth and a SIAM data-science audience.
Route broader applied/data-mining papers to ICDM/KDD, database-system work to ICDE/SIGMOD/VLDB, and general ML algorithms to the ML flagships.
SDM evidence should include formal problem structure, algorithmic analysis or principled modeling, rigorous experiments, and clarity about when the method should be used.
Method & evidence bar
Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
For SDM, the evidence must support the venue-specific signature: a data mining paper that benefits from SIAM-style rigor rather than product-style analytics framing.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.
Structure & house style
Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.
Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
If the live official instructions conflict with this skill, the official instructions win.
Pre-submission self-check
One sentence states why this manuscript belongs at SDM, using the venue's scope rather than generic "top conference" language.
The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
Related work includes the nearest current-cycle data mining papers and explains the technical delta.
The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.
Common desk-reject triggers
Leaderboard-only novelty with weak explanation of why the method works.
Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
Claims about safety, fairness, health, or society without matching evidence and limitations.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving SDM reviewers too little technical or empirical substance.
Re-routing decision
If the paper misses SDM's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
Output format
[Fit] High / Medium / Low (one-line reason)
[Target] SIAM International Conference on Data Mining (SDM)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>
