{ "schema_version": "core-reviewed-term-ai-handoff-v1", "version": "core-reviewed-term-ai-handoff-v1", "contract_status": "preview", "distribution": "per_term_read_only_ai_handoff", "no_write_operations": true, "canonical": { "url": "https://core.yogoq.com/en-US/core/data-mining", "slug": "data-mining", "locale": "en-US" }, "summary": { "schema_version": "core-reviewed-term-summary-v1", "term_id": "66d460b9-1932-575c-a06e-a9b8318c6a5e", "canonical_slug": "data-mining", "canonical_url": "https://core.yogoq.com/en-US/core/data-mining", "locale": "en-US", "display_name": "Data Mining", "english_name": null, "abbreviation": null, "short_definition": "Data mining is the process of discovering patterns and relationships in large datasets using statistical and machine learning methods.", "quality": "reviewed", "publication_status": "published_reviewed", "version": "core-reviewed-term-summary-v1", "last_reviewed_at": "2026-05-14T00:00:00.000Z", "sources_count": 3, "limitations_key": "core-trust-policy-v1-2026-06-22" }, "content": { "definition": { "key": "definition", "title": "一言でいうと", "text": "Data mining is the process of discovering patterns and relationships in large datasets using statistical and machine learning methods.", "items": [] }, "formula": null, "boundary": null, "usage": [ { "key": "meaning", "title": "意味", "text": "Data mining applies algorithms to uncover trends, clusters, associations, or anomalies that are not obvious in raw data. It typically involves preparing data, selecting models, and validating results to avoid false patterns. Successful data mining links discovered patterns to business questions and operational actions.", "items": [] }, { "key": "usage", "title": "役立つ場面", "text": "It determines which use cases are feasible for pattern discovery. It influences data preparation and feature selection priorities. It shapes how insights are operationalized in products or processes.", "items": [ "It determines which use cases are feasible for pattern discovery.", "It influences data preparation and feature selection priorities.", "It shapes how insights are operationalized in products or processes." ] }, { "key": "usage", "title": "使い方のポイント", "text": null, "items": [ "Define a clear objective before mining to avoid meaningless patterns.", "Invest in data cleaning and feature engineering for accuracy.", "Validate findings with holdout data to reduce false discovery.", "Interpret results with domain expertise to ensure relevance.", "Monitor models because patterns can change over time." ] } ], "misunderstandings": [ { "key": "misunderstandings", "title": "よくある誤解 / 落とし穴", "text": null, "items": [ "Data mining does not guarantee useful insights without a clear goal.", "Algorithms cannot replace domain knowledge and context.", "More data does not automatically produce better patterns." ] } ], "examples": [ { "key": "examples", "title": "最小例", "text": "A retailer analyzes transaction data to find products often purchased together. After cleaning item codes and removing anomalies, they apply association rules to identify bundles. The results are validated with a holdout sample and reviewed by category managers. The team then tests a cross-sell campaign and monitors whether the pattern holds over time.", "items": [] } ], "comparisons": [ { "key": "comparisons", "title": "似ている言葉との違い", "text": "Compare Data Mining with adjacent concepts before deciding. Data Mining | Current concept | Use when the team needs the primary decision lens Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail General vocabulary | Broad explanation | Use only for orientation, not final decision-making", "items": [ "Data Mining | Current concept | Use when the team needs the primary decision lens", "Adjacent metric or framework | Supporting lens | Use when the team needs evidence or process detail", "General vocabulary | Broad explanation | Use only for orientation, not final decision-making" ] } ], "faq": [ { "question": "When should I use Data Mining?", "answer": "Use it when the team needs to decide scope, priority, owner, or trade-off, not when it only needs a short definition." }, { "question": "What makes Data Mining useful in practice?", "answer": "It becomes useful when it is tied to evidence, a decision owner, and a concrete next operating choice." }, { "question": "What should I avoid?", "answer": "Avoid using the term as a label without clarifying assumptions, boundaries, and how success will be judged." } ] }, "sources": { "source_refs": [ "concept:data-mining:en-US", "concept:data-mining:ja-JP", "core-term-page-500:concept:data-mining" ], "visible_sources": [ { "label": "Principles of Data Science 6.5 Other Machine Learning Techniques (OpenStax)", "url": "https://openstax.org/books/principles-data-science/pages/6-5-other-machine-learning-techniques", "kind": null }, { "label": "Principles of Marketing (Open Textbook Library)", "url": "https://open.umn.edu/opentextbooks/textbooks/principles-of-marketing", "kind": "tier_s" }, { "label": "Principles of Management (OpenStax)", "url": "https://openstax.org/details/books/principles-management", "kind": "tier_s" } ] }, "related_terms": [ { "topic_id": "8d8b49be-dae4-5c0e-b2d5-7849e5da0e87", "canonical_slug": "dataset", "canonical_url": "https://core.yogoq.com/en-US/core/dataset", "title": "Dataset", "relation_type": "next" }, { "topic_id": "6e9c41ac-f7c6-5572-935d-fb340330ca97", "canonical_slug": "data-analysis", "canonical_url": "https://core.yogoq.com/en-US/core/data-analysis", "title": "Data Analysis", "relation_type": "used_together" }, { "topic_id": "bf1c072f-38a8-5b40-8194-ee8f53ce67a7", "canonical_slug": "3c-analysis", "canonical_url": "https://core.yogoq.com/en-US/core/3c-analysis", "title": "3C Analysis", "relation_type": "used_together" }, { "topic_id": "c2814f26-6ed6-50c2-bc84-a5e47d2c229a", "canonical_slug": "a-b-testing", "canonical_url": "https://core.yogoq.com/en-US/core/a-b-testing", "title": "A/B Testing", "relation_type": "used_together" } ], "quality": { "quality": "reviewed", "publication_status": "published_reviewed", "source_count": 3, "last_reviewed_at": "2026-05-14T00:00:00.000Z", "trust_policy_version": "core-trust-policy-v1-2026-06-22" }, "limitations": { "policy_version": "core-trust-policy-v1-2026-06-22", "professional_advice_boundary": "reference_only_not_professional_advice", "text": "This page is reference information for research and learning. For accounting, legal, finance, health, security, or other individual decisions, confirm against primary sources or qualified professionals.", "items": [ "Public pages support general understanding and practical context; they are not professional advice for individual cases.", "Fast-changing information such as regulations, accounting standards, prices, product specs, and legal requirements should be checked against primary sources before final decisions.", "Even when AI-assisted drafting or audit is used, publication relies on quality gates and human-readable evidence." ] } }