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acm-sigkdd-conference-on-knowledge-discovery-and-data-mining

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Use when targeting ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for data mining.

When to use

• targeting ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• deciding whether a computer-science manuscript fits this venue
• conference fit, framing, evidence bar, submission-cycle checks
• rebuttal posture, and desk-reject risks for data mining
• manuscript in data mining needs a conference-fit read
• re-framed from journal style or arXiv style into a selective CS conference narrative
• evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis

When not to use

• substitute for the current year's CFP, author kit, ethics policy, or submission portal
• planning, scheduling, temporal reasoning, or planner search is the central contribution
• novelty is a general learning method rather than data-discovery or applied-scale evidence

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