public static List <AssociationRule> Mine(ItemsetCollection db, ItemsetCollection l, double confidenceThreshold)
{
var allRules = new List <AssociationRule>();
foreach (Itemset itemset in l)
{
var subsets = Bit.FindSubsets(itemset, 0); //get all subsets
foreach (var subset in subsets)
{
var confidence = (db.FindSupport(itemset) / db.FindSupport(subset)) * 100.0;
if (confidence >= confidenceThreshold)
{
var 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);
}
public static ItemsetCollection DoApriori(ItemsetCollection db, double supportThreshold)
{
var I = db.GetUniqueItems();
var l = new ItemsetCollection(); //resultant large itemsets
var li = new ItemsetCollection(); //large itemset in each iteration
var ci = new ItemsetCollection(); //pruned itemset in each iteration
ci.AddRange(I.Select(item => new Itemset {
item
}));
//first iteration (1-item itemsets)
//next iterations
var k = 2;
while (ci.Count != 0)
{
//set Li from Ci (pruning)
li.Clear();
foreach (var itemset in ci)
{
itemset.Support = db.FindSupport(itemset);
if (itemset.Support >= supportThreshold)
{
li.Add(itemset);
l.Add(itemset);
}
}
//set Ci for next iteration (find supersets of Li)
ci.Clear();
ci.AddRange(Bit.FindSubsets(li.GetUniqueItems(), k)); //get k-item subsets
k += 1;
}
return(l);
}