// this is the ap-genrules procedure that generates rules out
// of a frequent itemset.
private void ap_genrules(Itemset is_frequent, Vector consequents) {
if (consequents.size() == 0) return;
// the size of frequent must be bigger than the size of the itemsets
// in consequents by at least 2, in order to be able to generate
// a rule in this call
if (is_frequent.size() > ((Itemset) (consequents.get(0))).size() + 1) {
Vector new_consequents = apriori_gen(consequents);
AssociationRule ar;
for (int i = 0; i < new_consequents.size(); i++) {
Itemset is_consequent = (Itemset) new_consequents.get(i);
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
// if the rule satisfies our requirements we add it
// to our collection
if (confidence >= min_confidence)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
// otherwise we remove the consequent from the collection
// and we update the index such that we don't skip a consequent
else new_consequents.remove(i--);
}
ap_genrules(is_frequent, new_consequents);
}
}
// this is the apriori_gen procedure that generates starting from
// a k-itemset collection a new collection of (k+1)-itemsets.
private Vector apriori_gen(Vector itemsets) {
if (itemsets.size() == 0) return new Vector(0);
// create a hashtree so that we can check more efficiently the
// number of subsets
// this may not really be necessary when generating rules since
// itemsets will probably be a small collection, but just in case
HashTree ht_itemsets = new HashTree(itemsets);
for (int i = 0; i < itemsets.size(); i++) ht_itemsets.add(i);
ht_itemsets.prepareForDescent();
Vector result = new Vector();
Itemset is_i, is_j;
for (int i = 0; i < itemsets.size() - 1; i++)
for (int j = i + 1; j < itemsets.size(); j++) {
is_i = (Itemset) itemsets.get(i);
is_j = (Itemset) itemsets.get(j);
// if we cannot combine element i with j then we shouldn't
// waste time for bigger j's. This is because we keep the
// collections ordered, an important detail in this implementation
if (!is_i.canCombineWith(is_j)) break;
else {
Itemset is = is_i.combineWith(is_j);
// a real k-itemset has k (k-1)-subsets
// so we test that this holds before adding to result
if (ht_itemsets.countSubsets(is) == is.size()) result.add(is);
}
}
return result;
}
private void generateRules(ArrayList<Itemset> Lk, int clas) {
int i, uncover;
Itemset itemset;
double confidence[] = new double[2];
double var1[] = new double[2];
double var2[] = new double[2];
for (i = Lk.size() - 1; i >= 0; i--) {
itemset = Lk.get(i);
var2 = itemset.getSupport();
if (var2[0] > 0.0) {
var1 = itemset.getSupportClass();
confidence[0] = var1[0] / var2[0];
confidence[1] = var1[1] / var2[1];
} else {
confidence[0] = confidence[1] = 0.0;
}
if (confidence[0] > 0.4) {
this.ruleBaseClase.add(itemset);
ruleStage1++;
}
if (confidence[0] > this.minconf) Lk.remove(i);
}
if (this.ruleBaseClase.size() > 500000) {
this.ruleBaseClase.reduceRules(clas);
// System.out.println("Number of rules: " + this.ruleBase.size());
System.gc();
}
}
// Generate C(k+1) by join itemset-pairs in F(k)
private static List<Itemset> generateCandidates(List<Itemset> frequentItemsets) {
if (frequentItemsets.isEmpty() || frequentItemsets.size() == 1) {
return new ArrayList<>();
}
Collections.sort(frequentItemsets);
List<Itemset> candidates = new ArrayList<>();
Itemset candidate;
for (int i = 0, j = 1; i != frequentItemsets.size(); ) {
while (j != frequentItemsets.size()
&& Itemset.generateCandidate(frequentItemsets.get(i), frequentItemsets.get(j)) != null) {
++j;
}
for (int k = i; k != j; ++k) {
for (int l = k + 1; l != j; ++l) {
Itemset itemset =
(Itemset.generateCandidate(frequentItemsets.get(k), frequentItemsets.get(l)));
assert itemset != null;
candidates.add(itemset);
}
}
i = j;
j++;
}
return candidates;
}
/**
* Find association rules in a database, given the set of frequent itemsets.
*
* @param cacheReader the object used to read from the cache
* @param minSupport the minimum support
* @param minConfidence the minimum confidence
* @return a Vector containing all association rules found
*/
public Vector findAssociations(DBCacheReader cacheReader, float minSupport, float minConfidence) {
min_support = minSupport;
min_confidence = minConfidence;
// create the vector where we'll put the rules
rules = new Vector();
// read from cache supports of frequent itemsets
initializeSupports(cacheReader);
// get the frequent itemsets
Vector frequent = supports.getItemsets();
// generate rules from each frequent itemset
for (int i = 0; i < frequent.size(); i++) {
// get a frequent itemset
Itemset is_frequent = (Itemset) frequent.get(i);
// skip it if it's too small
if (is_frequent.size() <= 1) continue;
// get all possible 1 item consequents
Vector consequents = new Vector(is_frequent.size());
for (int k = 0; k < is_frequent.size(); k++) {
int item = is_frequent.getItem(k);
Itemset is_consequent = new Itemset(1);
is_consequent.addItem(item);
// is_consequent now contains a possible consequent
// verify now that the rule having this consequent
// satisfies our requirements
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
if (confidence >= min_confidence) {
consequents.add(is_consequent);
// we add the rule to our collection if it satisfies
// our conditions
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
}
// call the ap_genrules procedure for generating all rules
// out of this frequent itemset
ap_genrules(is_frequent, consequents);
}
return rules;
}
// Prune itemsets from C(k+1) that violate downward closure
private static List<Itemset> prune(List<Itemset> candidates, List<Itemset> frequentItemsets) {
List<Itemset> prunedCandicates = new ArrayList<>();
for (Itemset candidate : candidates) {
if (frequentItemsets.containsAll(candidate.downwardClosure())) {
prunedCandicates.add(candidate);
}
}
return prunedCandicates;
}
private void generateLarge(ArrayList<Itemset> Lk, int clas) {
int i, j, size;
ArrayList<Itemset> Lnew;
Itemset newItemset, itemseti, itemsetj;
size = Lk.size();
if (size > 1) {
if (((Lk.get(0)).size() < this.nVariables) && ((Lk.get(0)).size() < this.depth)) {
Lnew = new ArrayList<Itemset>();
for (i = 0; i < size - 1; i++) {
itemseti = Lk.get(i);
for (j = i + 1; j < size; j++) {
itemsetj = Lk.get(j);
if (this.isCombinable(itemseti, itemsetj)) {
newItemset = itemseti.clone();
newItemset.add((itemsetj.get(itemsetj.size() - 1)).clone());
newItemset.calculateSupports(this.dataBase, this.train);
if (newItemset.getSupportClass()[0] >= this.minsup) Lnew.add(newItemset);
}
}
this.generateRules(Lnew, clas);
this.generateLarge(Lnew, clas);
Lnew.clear();
System.gc();
}
}
}
}
private boolean isCombinable(Itemset itemseti, Itemset itemsetj) {
int i;
Item itemi, itemj;
Itemset itemset;
itemi = itemseti.get(itemseti.size() - 1);
itemj = itemsetj.get(itemseti.size() - 1);
if (itemi.getVariable() >= itemj.getVariable()) return (false);
return (true);
}
/**
* Function to add the given itemset to given the value.
*
* @param valueIndex The index of the value.
* @param itemset The itemset to add.
*/
public final void add(int valueIndex, Itemset itemset) {
int classIndex;
double weight;
classIndex = (int) itemset.getClassValue();
weight = itemset.getWeight();
perClassPerValue[valueIndex][classIndex] = perClassPerValue[valueIndex][classIndex] + weight;
perValue[valueIndex] = perValue[valueIndex] + weight;
perClass[classIndex] = perClass[classIndex] + weight;
total = total + weight;
}
// this is the ap-genrules procedure that generates rules out
// of a frequent itemset.
private void ap_genrules_constraint(Itemset is_frequent, Vector consequents) {
if (consequents.size() == 0) return;
// the size of frequent must be bigger than the size of the itemsets
// in consequents by at least 2, in order to be able to generate
// a rule in this call
if (is_frequent.size() > ((Itemset) (consequents.get(0))).size() + 1) {
Vector new_consequents = apriori_gen(consequents);
AssociationRule ar;
for (int i = 0; i < new_consequents.size(); i++) {
Itemset is_consequent = (Itemset) new_consequents.get(i);
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
// if the rule satisfies our confidence requirements
if (confidence >= min_confidence) {
// check whether it also satisfies our constraints
boolean approved = true;
if (approved && is_in_antecedent != null && !is_in_antecedent.isIncludedIn(is_antecedent))
approved = false;
if (approved && is_in_consequent != null && !is_in_consequent.isIncludedIn(is_consequent))
approved = false;
if (approved && max_antecedent > 0 && is_antecedent.size() > max_antecedent)
approved = false;
if (approved && min_consequent > 0 && is_consequent.size() < min_consequent)
approved = false;
// if the rule satisifes all requirements then
// we add it to the rules collection
if (approved)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
// otherwise we remove the consequent from the collection
// and we update the index such that we don't skip a consequent
else new_consequents.remove(i--);
}
ap_genrules_constraint(is_frequent, new_consequents);
}
}
/**
* Function to read an itemset and appends it to the dataset.
*
* @return True if the itemset was readed succesfully.
*/
private boolean getItemsetFull() {
// fill itemset
for (int j = 0; j < IS.getNumInstances(); j++) {
double[] itemset = new double[Attributes.getNumAttributes()];
int index;
// Get values for all input attributes.
for (int i = 0; i < Attributes.getInputNumAttributes(); i++) {
// check type and if there is null
if (IS.getInstance(j).getInputMissingValues(i)) itemset[i] = Itemset.getMissingValue();
else {
if (Attributes.getInputAttribute(i).getType() == 0) // nominal
{
for (int k = 0; k < Attributes.getAttribute(i).getNumNominalValues(); k++)
if (Attributes.getAttribute(i)
.getNominalValue(k)
.equals(IS.getInstance(j).getInputNominalValues(i))) itemset[i] = (double) k;
} else // real and integer
{
itemset[i] = IS.getInstance(j).getInputRealValues(i);
}
} // else
} // for
// Get values for output attribute.
int i = Attributes.getInputNumAttributes();
// check type and if there is null
if (IS.getInstance(j).getOutputMissingValues(0)) itemset[i] = Itemset.getMissingValue();
else {
if (Attributes.getOutputAttribute(0).getType() == 0) // nominal
{
for (int k = 0; k < Attributes.getOutputAttribute(0).getNumNominalValues(); k++)
if (Attributes.getOutputAttribute(0)
.getNominalValue(k)
.equals(IS.getInstance(j).getOutputNominalValues(0))) itemset[i] = (double) k;
} else // real and integer
{
itemset[i] = IS.getInstance(j).getOutputRealValues(0);
}
} // else
// Add itemset to dataset
addItemset(new Itemset(1, itemset));
} // for
return true;
}
/**
* Function to check if the antecedent of our itemset is equal to another given
*
* @param a Itemset which antecedents we are going to compare with ours
* @return boolean true = they are equal, false = they aren't.
*/
public boolean isEqualAnt(Itemset a) {
int i;
Item item;
if (this.itemset.size() != a.size()) return (false);
for (i = 0; i < this.itemset.size(); i++) {
item = this.itemset.get(i);
if (!item.isEqual(a.get(i))) return (false);
}
return (true);
}
/**
* Funtion to add the given itemset to all values weighting it according to given weights.
*
* @param itemset The itemset to add.
* @param weights The weights of the itemset for every value.
*/
public final void addWeights(Itemset itemset, double[] weights) {
int classIndex;
int i;
classIndex = (int) itemset.getClassValue();
for (i = 0; i < perValue.length; i++) {
double weight = itemset.getWeight() * weights[i];
perClassPerValue[i][classIndex] = perClassPerValue[i][classIndex] + weight;
perValue[i] = perValue[i] + weight;
perClass[classIndex] = perClass[classIndex] + weight;
total = total + weight;
}
}
/**
* Function to shift all itemsets in given range from one value to another.
*
* @param from The minimum value.
* @param to The maximum value.
* @param source The dataset.
* @param start The index of the first itemset to add.
* @param end The index of the first itemset that will not be added.
*/
public final void shiftRange(int from, int to, Dataset source, int start, int end) {
int classIndex;
double weight;
Itemset itemset;
int i;
for (i = start; i < end; i++) {
itemset = (Itemset) source.itemset(i);
classIndex = (int) itemset.getClassValue();
weight = itemset.getWeight();
perClassPerValue[from][classIndex] -= weight;
perClassPerValue[to][classIndex] += weight;
perValue[from] -= weight;
perValue[to] += weight;
}
}
/**
* Function to add all itemsets in given range to given value.
*
* @param valueIndex The index of the value.
* @param itemset The itemset to add.
* @param start The index of the first itemset to add.
* @param end The index of the first itemset that will not be added.
* @throws Exception
*/
public final void addRange(int valueIndex, Dataset source, int start, int end) {
double sumOfWeights = 0;
int classIndex;
Itemset itemset;
int i;
for (i = start; i < end; i++) {
itemset = (Itemset) source.itemset(i);
classIndex = (int) itemset.getClassValue();
sumOfWeights = sumOfWeights + itemset.getWeight();
perClassPerValue[valueIndex][classIndex] += itemset.getWeight();
perClass[classIndex] += itemset.getWeight();
}
perValue[valueIndex] += sumOfWeights;
total += sumOfWeights;
}
/** Clone function */
public Itemset clone() {
Itemset d = new Itemset(this.clas);
for (int i = 0; i < this.itemset.size(); i++) d.add((itemset.get(i)).clone());
d.clas = this.clas;
d.support = this.support;
d.supportRule = this.supportRule;
d.per = this.per;
d.hits = this.hits;
d.misses = this.misses;
return (d);
}
public int hasUncoverClass(int clas) {
int uncover;
double degree[] = new double[2];
Itemset itemset;
boolean stop;
uncover = 0;
for (int j = 0; j < train.size(); j++) {
if (this.train.getOutputAsInteger(j) == clas) {
stop = false;
for (int i = 0; i < L2.size() && !stop; i++) {
itemset = L2.get(i);
degree = itemset.degree(this.dataBase, this.train.getExample(j));
if (degree[0] > 0.0) stop = true;
}
if (!stop) uncover++;
}
}
return uncover;
}
// this method stores all frequent itemsets that have support
// greater than the minimum support in a SET for more efficient
// access times.
private void initializeSupports(DBCacheReader cacheReader) {
// create new SET
supports = new SET();
try {
Itemset is;
while (true) {
// get item from cache
is = cacheReader.getNextItemset();
// if item has support greater than the minimum support
// required then we add it to the SET
if (is.getSupport() >= min_support) {
supports.insert(is);
}
}
} catch (EOFException e) {
// do nothing, we just reached the EOF
} catch (IOException e) {
System.err.println("Error scanning cache!!!\n" + e);
} catch (ClassNotFoundException e) {
System.err.println("Error scanning cache!!!\n" + e);
}
}
/**
* It adds a sequence from an array of string that we have to interpret
*
* @param integers
* @param sequenceID
*/
public void addSequence(String[] integers, int sequenceID) {
long timestamp = -1;
Sequence sequence = new Sequence(sequences.size());
sequence.setID(sequenceID);
Itemset itemset = new Itemset();
int inicio = 0;
Map<Item, Boolean> counted = new HashMap<Item, Boolean>();
for (int i = inicio; i < integers.length; i++) {
if (integers[i].codePointAt(0) == '<') { // Timestamp
String value = integers[i].substring(1, integers[i].length() - 1);
timestamp = Long.parseLong(value);
itemset.setTimestamp(timestamp);
} else if (integers[i].equals("-1")) { // end of an itemset
long time = itemset.getTimestamp() + 1;
sequence.addItemset(itemset);
itemset = new Itemset();
itemset.setTimestamp(time);
} else if (integers[i].equals("-2")) { // end of a sequence
sequences.add(sequence);
} else {
// extract the value for an item
Item item = itemFactory.getItem(Integer.parseInt(integers[i]));
if (counted.get(item) == null) {
counted.put(item, Boolean.TRUE);
BitSet appearances = frequentItems.get(item);
if (appearances == null) {
appearances = new BitSet();
frequentItems.put(item, appearances);
}
appearances.set(sequence.getId());
}
itemset.addItem(item);
}
}
}
/**
* Function to add all itemsets with unknown values for given attribute.
*
* @param source The dataset that contains all the itemsets.
* @param attIndex The index of the attribute with possible unknown values.
* @throws Exception
*/
public final void addWithUnknownValue(Dataset source, int attIndex) {
double[] probs;
double weight, newWeight;
int classIndex;
Itemset itemset;
int j;
probs = new double[perValue.length];
for (j = 0; j < perValue.length; j++) {
// if ( Comparators.isEqual( total, 0 ) )
if (total == 0) {
probs[j] = 1.0 / probs.length;
} else {
probs[j] = perValue[j] / total;
}
}
Enumeration enum2 = source.enumerateItemsets();
while (enum2.hasMoreElements()) {
itemset = (Itemset) enum2.nextElement();
if (itemset.isMissing(attIndex)) {
classIndex = (int) itemset.getClassValue();
weight = itemset.getWeight();
perClass[classIndex] = perClass[classIndex] + weight;
total = total + weight;
for (j = 0; j < perValue.length; j++) {
newWeight = probs[j] * weight;
perClassPerValue[j][classIndex] = perClassPerValue[j][classIndex] + newWeight;
perValue[j] = perValue[j] + newWeight;
}
}
}
}
/**
* Function to check if our itemset is Subitemset (can be contained) of a given itemset
*
* @param a Itemset to check if can contain ours
* @return boolean true = our itemset is subitemset of a, false = it isn't.
*/
public boolean isSubItemset(Itemset a) {
int i, j;
Item itemi, itemj;
boolean stop;
if (this.clas != a.getClas()) return (false);
for (i = 0; i < this.itemset.size(); i++) {
itemi = this.itemset.get(i);
stop = false;
for (j = 0; j < a.itemset.size() && !stop; j++) {
itemj = a.itemset.get(j);
if (itemi.isEqual(itemj)) stop = true;
else if (itemj.getVariable() >= itemi.getVariable()) return (false);
}
if (!stop) return (false);
}
return (true);
}
/**
* It adds a rule to the rule base
*
* @param itemset itemset to be added
* @param time Time of the rule
*/
public void add(Itemset itemset, long time) {
int i;
Item item;
int[] antecedent = new int[n_variables];
for (i = 0; i < n_variables; i++) antecedent[i] = -1; // Don't care
for (i = 0; i < itemset.size(); i++) {
item = itemset.get(i);
antecedent[item.getVariable()] = item.getValue();
}
Rule r = new Rule(this.dataBase);
r.asignaAntecedente(antecedent);
r.setConsequent(itemset.getClas());
r.setConfidence(itemset.getSupportClass() / itemset.getSupport());
r.setSupport(itemset.getSupportClass());
r.setTime(time);
this.ruleBase.add(r);
}
private void generateL2(int clas) {
int i, j, k, uncover;
Item item;
Itemset itemset;
this.L2.clear();
itemset = new Itemset(clas);
for (i = 0; i < this.nVariables; i++) {
if (this.dataBase.numLabels(i) > 1) {
for (j = 0; j < this.dataBase.numLabels(i); j++) {
item = new Item(i, j);
itemset.add(item);
itemset.calculateSupports(this.dataBase, this.train);
if (itemset.getSupportClass()[0] >= this.minsup) this.L2.add(itemset.clone());
itemset.remove(0);
}
}
}
this.generateRules(this.L2, clas);
}
/**
* Function to add one itemset.
*
* @param itemset The itemset to add to the dataset.
*/
public final void addItemset(Itemset itemset) {
Itemset newItemset = (Itemset) itemset.copy();
newItemset.setDataset(this);
itemsets.addElement(newItemset);
}
/**
* Find association rules in a database, given the set of frequent itemsets and a set of
* restrictions.
*
* @param cacheReader the object used to read from the cache
* @param minSupport the minimum support
* @param minConfidence the minimum confidence
* @param inAntecedent the items that must appear in the antecedent of each rule, if null then
* this constraint is ignored
* @param inConsequent the items that must appear in the consequent of each rule, if null then
* this constraint is ignored
* @param ignored the items that should be ignored, if null then this constraint is ignored
* @param maxAntecedent the maximum number of items that can appear in the antecedent of each
* rule, if 0 then this constraint is ignored
* @param minConsequent the minimum number of items that should appear in the consequent of each
* rule, if 0 then this constraint is ignored
* @return a Vector containing all association rules found
*/
public Vector findAssociations(
DBCacheReader cacheReader,
float minSupport,
float minConfidence,
Itemset inAntecedent,
Itemset inConsequent,
Itemset ignored,
int maxAntecedent,
int minConsequent) {
min_support = minSupport;
min_confidence = minConfidence;
is_in_antecedent = inAntecedent;
is_in_consequent = inConsequent;
is_ignored = ignored;
max_antecedent = maxAntecedent;
min_consequent = minConsequent;
// create the vector where we'll put the rules
rules = new Vector();
// read from cache supports of frequent itemsets
initializeSupports(cacheReader);
// get the frequent itemsets
Vector frequent = supports.getItemsets();
if (frequent.size() == 0) return rules;
// if we need to ignore some items
if (ignored != null) {
// remove all frequent itemsets that contain
// items to be ignored; their subsets that do
// not contain those items will remain
for (int i = 0; i < frequent.size(); i++) {
Itemset is = (Itemset) frequent.get(i);
if (is.doesIntersect(ignored)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
}
}
if (frequent.size() == 0) return rules;
}
// if we need to have some items in the antecedent or consequent
if (inAntecedent != null || inConsequent != null) {
// remove frequent itemsets that don't have the
// required items
for (int i = 0; i < frequent.size(); i++) {
Itemset is = (Itemset) frequent.get(i);
if (inAntecedent != null && !inAntecedent.isIncludedIn(is)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
} else if (inConsequent != null && !inConsequent.isIncludedIn(is)) {
// replace this element with last, delete last,
// and don't advance index
frequent.set(i, frequent.lastElement());
frequent.remove(frequent.size() - 1);
i--;
}
}
if (frequent.size() == 0) return rules;
}
// generate rules from each frequent itemset
for (int i = 0; i < frequent.size(); i++) {
// get a frequent itemset
Itemset is_frequent = (Itemset) frequent.get(i);
// skip it if it's too small
if (is_frequent.size() <= 1 || is_frequent.size() <= minConsequent) continue;
// get all possible 1 item consequents
Vector consequents = new Vector(is_frequent.size());
for (int k = 0; k < is_frequent.size(); k++) {
int item = is_frequent.getItem(k);
Itemset is_consequent = new Itemset(1);
is_consequent.addItem(item);
// is_consequent now contains a possible consequent
// verify now that the rule having this consequent
// satisfies our requirements
Itemset is_antecedent = is_frequent.subtract(is_consequent);
float antecedent_support = (float) 0.00001;
try {
antecedent_support = supports.getSupport(is_antecedent);
} catch (SETException e) {
System.err.println("Error geting support from SET!!!\n" + e);
}
float confidence = is_frequent.getSupport() / antecedent_support;
if (confidence >= min_confidence) {
consequents.add(is_consequent);
// check whether it also satisfies our constraints
boolean approved = true;
if (approved && is_in_antecedent != null && !is_in_antecedent.isIncludedIn(is_antecedent))
approved = false;
if (approved && is_in_consequent != null && !is_in_consequent.isIncludedIn(is_consequent))
approved = false;
if (approved && max_antecedent > 0 && is_antecedent.size() > max_antecedent)
approved = false;
if (approved && min_consequent > 0 && is_consequent.size() < min_consequent)
approved = false;
// if the rule satisifes all requirements then
// we add it to the rules collection
if (approved)
rules.add(
new AssociationRule(
is_antecedent, is_consequent, is_frequent.getSupport(), confidence));
}
}
// call the ap-genrules procedure for generating all rules
// out of this frequent itemset
ap_genrules_constraint(is_frequent, consequents);
}
return rules;
}