Random Market Basket Transaction Dataset Generator

Description

Compose synthetic baskets from supplied co-purchase templates and independent background draws, retaining planted labels and delegated mining evidence with unrounded count metrics.

Synthetic statistical data separates a specified generating model from the values observed in a finite draw. A model parameter, a sample summary, and the result of a fitted analysis answer different questions; retaining them separately makes numerical and statistical checks easier to interpret.

Compose synthetic baskets from supplied co-purchase templates and independent background draws, retaining planted labels and delegated mining evidence with unrounded count metrics.

When to use this tool

Use this generator to create reproducible market basket transaction dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply items, templates, transactionCount, templateProbability, backgroundProbability, minimumSupport, minimumConfidence, maximumSize in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read transactions, mining. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

For each transaction, optionally plant a supplied item template, independently add background items, and ensure the basket is nonempty. Sort distinct items and delegate frequent-itemset and association-rule evidence under the supplied support and confidence thresholds.^[1]

Input field	Meaning and units	Accepted input
items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 6.
templates	Templates	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 8.
templates[].label	Caller-facing text label; not a globally unique identifier.	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
templates[].items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 6.
transactionCount	Transaction Count	Required. Safe integer; minimum 1; maximum 128.
templateProbability	Template Probability	Required. Finite number; minimum 0; maximum 1.
backgroundProbability	Background Probability	Required. Finite number; minimum 0; maximum 1.
minimumSupport	Minimum Support	Required. Finite number; maximum 1; strictly above 0.
minimumConfidence	Minimum Confidence	Required. Finite number; minimum 0; maximum 1.
maximumSize	Maximum Size	Required. Safe integer; minimum 2; maximum 3.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
transactions	Transactions	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 128.
transactions[].items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 6.
transactions[].plantedTemplate	Planted Template	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64. or Null.
mining	Mining	Required. Object; no unknown keys.
mining.itemsets	Itemsets	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 41.
mining.itemsets[].items	Items	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 3.
mining.itemsets[].count	Requested count; the mechanics explain whether one draw expands into several returned records.	Required. Safe integer; minimum 0; maximum 128.
mining.itemsets[].support	Support	Required. Finite number.
mining.itemsets[].coreSupport	Core Support	Required. Finite number.
mining.rules	Rules	Required. Array of Object; no unknown keys.; minimum items 0; maximum items 150.
mining.rules[].antecedent	Antecedent	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 3.
mining.rules[].consequent	Consequent	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 64.; minimum items 1; maximum items 3.
mining.rules[].support	Support	Required. Finite number.
mining.rules[].confidence	Confidence	Required. Finite number.
mining.rules[].lift	Lift	Required. Finite number.
mining.rules[].coreSupport	Core Support	Required. Finite number.
mining.rules[].coreConfidence	Core Confidence	Required. Finite number.
mining.rules[].coreLift	Core Lift	Required. Finite number.
randomness	Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.	Required. Object; no unknown keys.
randomness.seed	Optional numeric replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "items": [
      "bread",
      "milk"
    ],
    "templates": [
      {
        "label": "breakfast",
        "items": [
          "bread",
          "milk"
        ]
      }
    ],
    "transactionCount": 4,
    "templateProbability": 0.5,
    "backgroundProbability": 0.25,
    "minimumSupport": 0.25,
    "minimumConfidence": 0.5,
    "maximumSize": 2,
    "seed": 42
  },
  "output": {
    "transactions": [
      {
        "items": [
          "milk"
        ],
        "plantedTemplate": null
      },
      {
        "items": [
          "bread",
          "milk"
        ],
        "plantedTemplate": "breakfast"
      },
      {
        "items": [
          "milk"
        ],
        "plantedTemplate": null
      },
      {
        "items": [
          "bread"
        ],
        "plantedTemplate": null
      }
    ],
    "mining": {
      "itemsets": [
        {
          "items": [
            "bread"
          ],
          "count": 2,
          "support": 0.5,
          "coreSupport": 0.5
        },
        {
          "items": [
            "milk"
          ],
          "count": 3,
          "support": 0.75,
          "coreSupport": 0.75
        },
        {
          "items": [
            "bread",
            "milk"
          ],
          "count": 1,
          "support": 0.25,
          "coreSupport": 0.25
        }
      ],
      "rules": [
        {
          "antecedent": [
            "bread"
          ],
          "consequent": [
            "milk"
          ],
          "support": 0.25,
          "confidence": 0.5,
          "lift": 0.6666666666666666,
          "coreSupport": 0.25,
          "coreConfidence": 0.5,
          "coreLift": 0.666666666667
        }
      ]
    },
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

A planted template label records construction, not a mined result. Sampling noise and background items can create other associations; this is synthetic transaction evidence rather than consumer behavior or causal inference.
A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Market Basket Transaction Dataset Generator — tool.io

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