public static List <AssociationRule> Mine(ItemsetCollection db, ItemsetCollection L, double confidenceThreshold)
{
var allRules = new List <AssociationRule>();
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)
{
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 FindSubsets(Itemset itemset, int n)
{
var subsets = new ItemsetCollection();
int subsetCount = (int)Math.Pow(2, itemset.Count);
for (int i = 0; i < subsetCount; i++)
{
if (n == 0 || GetOnCount(i, itemset.Count) == n)
{
string binary = DecimalToBinary(i, itemset.Count);
Itemset subset = new Itemset();
for (int charIndex = 0; charIndex < binary.Length; charIndex++)
{
if (binary[charIndex] == '1')
{
subset.Add(itemset[charIndex]);
}
}
subsets.Add(subset);
}
}
return(subsets);
}
/// <summary>
/// Executes the algorithm
/// </summary>
/// <param name="db">The collection of transactions</param>
/// <param name="uniqueItems">The item set that contains the total number of items (50 in our case)</param>
/// <param name="supportThreshold">The minimum support</param>
/// <returns>An item set collection</returns>
public static ItemsetCollection DoApriori(ItemsetCollection db, Itemset uniqueItems, double supportThreshold)
{
// var I = db.GetUniqueItems();
var I = uniqueItems;
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
// first iteration (1-item itemsets)
foreach (var item in I)
{
Ci.Add(new Itemset()
{
item
});
}
// next iterations
var k = 2;
while (Ci.Count != 0)
{
// set Li from Ci (pruning)
Li.Clear();
foreach (Itemset 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);
}
