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 FindSubsets(Itemset itemset, int n) { var subsets = new ItemsetCollection(); var subsetCount = (int)Math.Pow(2, itemset.Count); for (var i = 0; i < subsetCount; i++) { if (n == 0 || GetOnCount(i, itemset.Count) == n) { var binary = DecimalToBinary(i, itemset.Count); var subset = new Itemset(); for (var charIndex = 0; charIndex < binary.Length; charIndex++) { if (binary[charIndex] == '1') { subset.Add(itemset[charIndex]); } } subsets.Add(subset); } } return(subsets); }
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); }