data-mining.md

+# Data-mining
+
+Data mining with Choco Solver.
+
+## Constraints
+
+The following constraints are available:
+
+
+
+`AdequateClosureDC(Database database, List<Measure> measures, BoolVar[] items)`
+
+**Parameters**:
+
+- A transactional database `database`
+- A list of measures `measures` (see  Measures section for more information on available measures)
+- An array of Boolean variables `items` where `items[i]` is true iff item `i` belongs to the pattern
+
+**Description**: Ensure that the pattern represented by `items` is closed w.r.t. the set of measures `measures` (Domain Consistency version)
+
+**References**: `Vernerey et al. - Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules`
+
+
+
+`AdequateClosureWC(Database database, List<Measure> measures, BoolVar[] items)`
+
+**Parameters**:
+
+- A transactional database `database`
+- A list of measures `measures` (see  Measures section for more information on available measures)
+- An array of Boolean variables `items` where `items[i]` is true iff item `i` belongs to the searched pattern
+
+**Description**: Ensure that the pattern represented by `items` is closed w.r.t. the set of measures `measures` (Weak Consistency version)
+
+**References**: `Vernerey et al. - Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules`
+
+
+
+`CoverClosure(Database database, BoolVar[] items)`
+
+**Parameters**:
+
+- A transactional database `database`
+- An array of Boolean variables `items` where `items[i]` is true iff item `i` belongs to the searched pattern
+
+**Description**: Ensure that the pattern represented by `items` is closed w.r.t. the support
+
+**References**: `Schaus et al. - CoverSize : A Global Constraint for Frequency-Based Itemset Mining`
+
+
+
+`CoverSize(Database database, IntVar freq, BoolVar[] items)`
+
+**Parameters**:
+
+- A transactional database `database`
+- An integer variable `freq` that represents the frequency of the pattern
+- An array of Boolean variables `items` where `items[i]` is true iff item `i` belongs to the pattern
+
+**Description**: Ensure that the variable `freq` is equal to the frequency of the pattern represented by `items` variables
+
+**References**: `Schaus et al. - CoverSize : A Global Constraint for Frequency-Based Itemset Mining`
+
+
+
+`FrequentSubs(Database database, int freq, BoolVar[] x)`
+
+**Parameters**:
+
+- A transactional database `database`
+- A threshold `freq` 
+- An array of Boolean variables `x` where `x[i]` is true iff item `i` belongs to the pattern
+
+**Description**: Ensure that all the subsets of `x` are frequent w.r.t. the `freq` threhsold (i.e. frequency(y) >= freq for all y subsets of x)
+
+**References**: `Belaid et al. - Constraint Programming for Mining Borders of Frequent Itemsets`
+
+
+
+`Generator(Database database, BoolVar[] items)`
+
+**Parameters**:
+
+- A transactional database `database`
+- An array of Boolean variables `items` where `items[i]` is true iff item `i` belongs to the pattern
+
+**Description**: Ensure that the pattern represented by `items` is a generator (i.e. has no subset with the same frequency)
+
+**References**: `Belaid et al. - Constraint Programming for Association Rules`
+
+
+
+`InfrequentSupers(Database database, int freq, BoolVar[] x)`
+
+**Parameters**:
+
+- A transactional database `database`
+- A threshold `freq` 
+- An array of Boolean variables `x` where `x[i]` is true iff item `i` belongs to the pattern
+
+**Description**: Ensure that all the supersets of `x` are infrequent w.r.t. the `freq` threhsold (i.e. frequency(y) < freq for all y supersets of x)
+
+**References**: `Belaid et al. - Constraint Programming for Mining Borders of Frequent Itemsets`
+
+
+
+## Measures
+
+The following measures are available (see the package `io.gitlab.chaver.mining.patterns.measure`) :
+
+- Pattern measures:
+  - `AllConf`: All-confidence
+  - `Area`
+  - `Freq`
+  - `Freq1`
+  - `Freq2`
+  - `FreqNeg`
+  - `GrowthRate`
+  - `Length`
+  - `MaxFreq`
+- Attribute measures:
+  - `Max`
+  - `Min`
+  - `Mean`
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