Cross-industry standard process for data mining¶
A six-phase iterative process model for organizing data-mining projects from business understanding through deployment.
Core Idea¶
CRISP-DM organizes analytics work into business understanding, data understanding, data preparation, modeling, evaluation, and deployment, with iteration rather than a rigid one-way sequence. Each phase produces questions and artifacts that can force return to earlier phases, keeping technical modeling tied to business success and deployment constraints. The abstraction is therefore identified by a declared carrier, a transformation or constraint over that carrier, and an invariant that tells an analyst whether the named structure is genuinely present.
The load-bearing residual is not the broad topic of data mining. It is An organization may tailor tasks and outputs, but a generic machine-learning lifecycle that omits business framing or deployment is not CRISP-DM..
Scope of Application¶
Cross-industry standard process for data mining belongs to data mining and is useful where the analyst can specify a project objective, business context, data sources, preparation pipeline, models, evaluation criteria, deployment setting, and feedback between phases, then evaluate all six phases remain represented and transitions are driven by evidence and project objectives rather than treated as a purely linear recipe. The scope is broad within that domain but bounded by the need for all six phases remain represented and transitions are driven by evidence and project objectives rather than treated as a purely linear recipe. The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
Clarity¶
The abstraction clarifies a crowded vocabulary by making all six phases remain represented and transitions are driven by evidence and project objectives rather than treated as a purely linear recipe the center of the account. A claim should name the carrier, the governing operation or relation, the applicable assumptions, and the recognition test. A bare label is insufficient because the name Cross-industry standard process for data mining can be used for a formal identity, an implementation, or a neighboring result unless carrier and convention are stated.
Manages Complexity¶
Without the abstraction, an analyst must reason directly over many local details: the carrier roles, admissibility assumptions, competing conventions, derived invariants, boundary cases, and proof or validation obligations specific to Cross-industry standard process for data mining. Cross-industry standard process for data mining compresses them into the roles in the structural signature. That compression permits comparison across instances without erasing the variables that determine validity. It also exposes which details may be varied safely and which are constitutive.
Abstract Reasoning¶
- Identify the carrier. State what the elements, states, objects, or observations are: a project objective, business context, data sources, preparation pipeline, models, evaluation criteria, deployment setting, and feedback between phases. Reject examples whose alleged carrier belongs to a different problem. 2. Lock the constitutive rule. Express all six phases remain represented and transitions are driven by evidence and project objectives rather than treated as a purely linear recipe independently of one notation or implementation.
Knowledge Transfer¶
Knowledge transfers strongly among subfields of data mining because they reuse a project objective, business context, data sources, preparation pipeline, models, evaluation criteria, deployment setting, and feedback between phases, Each phase produces questions and artifacts that can force return to earlier phases, keeping technical modeling tied to business success and deployment constraints., and type the carrier, state every parameter and convention in the definition, test that all six phases remain represented and transitions are driven by evidence and project objectives rather than treated as a purely linear recipe, compare the nearest accepted identity, and report counterexamples, uncertainty, and limiting cases.
Relationships to Other Abstractions¶
Current abstraction Cross-industry standard process for data mining Domain-specific
Parents (1) — more general patterns this builds on
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Cross-industry standard process for data mining is a kind of Iteration Prime
The proposed strict upward parent is
prime:iteration.
Hierarchy path (1) — routes to 1 parentless root
- Cross-industry standard process for data mining → Iteration
Neighborhood in Abstraction Space¶
Cross-industry standard process for data mining sits in a moderately populated region (44th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.
Family — Project Planning & Process Maturity (7 abstractions)
Nearest neighbors
- Dimensional modeling — 0.90
- MoSCoW method — 0.89
- Process-data diagram — 0.89
- Evolutionary data mining — 0.89
- Decision table — 0.89
Computed from structural-signature embeddings · 2026-09-08