// 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;
}
