static ItemsetCollection AprioriCalc(ItemsetCollection db, double supportThreshold)
{
Itemset I = db.GetUniqueItems();
ItemsetCollection LitemList = new ItemsetCollection();
ItemsetCollection PitemList = new ItemsetCollection();
foreach (string item in I)
{
PitemList.Add(new Itemset()
{
item
});
}
int k = 2;
while (PitemList.Count != 0)
{
LitemList.Clear();
foreach (Itemset index in PitemList)
{
index.Support = db.FindSupport(index);
if (index.Support >= supportThreshold)
{
LitemList.Add(index);
itemList.Add(index);
}
}
PitemList.Clear();
PitemList.AddRange(Bit.FindSubsets(LitemList.GetUniqueItems(), k)); //get k-item subsets
k += 1;
}
return(itemList);
}
static List <AssociationRule> Rules(ItemsetCollection db, ItemsetCollection L, double confidenceThreshold)
{
foreach (Itemset itemset in L)
{
ItemsetCollection subsets = Bit.FindSubsets(itemset, 0); //get all subsets
foreach (Itemset subset in subsets)
{
double confidence = (db.FindSupport(itemset) / db.FindSupport(subset)) * 100.0;
if (confidence >= confidenceThreshold)
{
AssociationRule rule = new AssociationRule();
rule.X.AddRange(subset);
rule.Y.AddRange(itemset.Remove(subset));
rule.Support = db.FindSupport(itemset);
rule.Confidence = confidence;
if (rule.X.Count > 0 && rule.Y.Count > 0)
{
allRules.Add(rule);
}
}
}
}
return(allRules);
}