public List <List <Instance> > FindClusters(InstanceModel model, List <Instance> instances, out List <IEmergingPattern> selectedPatterns) { NominalFeature classFeature = null; FeatureInformation backupFeatureInformation = null; string[] backupClassValues = null; double[] backupClassByInstance = null; bool isClassPresent = true; if (model.ClassFeature() == null) { isClassPresent = false; classFeature = new NominalFeature("class", model.Features.Length); var backupFeatures = model.Features; model.Features = new Feature[backupFeatures.Length + 1]; for (int i = 0; i < backupFeatures.Length; i++) { model.Features[i] = backupFeatures[i]; } model.Features[backupFeatures.Length] = classFeature; } else { classFeature = model.ClassFeature() as NominalFeature; backupFeatureInformation = classFeature.FeatureInformation; backupClassValues = classFeature.Values; backupClassByInstance = new double[instances.Count]; for (int i = 0; i < instances.Count; i++) { backupClassByInstance[i] = instances[i][classFeature]; instances[i][classFeature] = 0; } } classFeature.FeatureInformation = new NominalFeatureInformation() { Distribution = new double[] { 1, 1, 1, 1, 1 }, Ratio = new double[] { 1, 1, 1, 1, 1 }, ValueProbability = new double[] { 1, 1, 1, 1, 1 } }; classFeature.Values = new string[1] { "Unknown" }; var Miner = new UnsupervisedRandomForestMiner() { ClusterCount = ClusterCount, TreeCount = 100 }; var patterns = Miner.Mine(model, instances, classFeature); var instIdx = new Dictionary <Instance, int>(); for (int i = 0; i < instances.Count; i++) { instIdx.Add(instances[i], i); } int[,] similarityMatrix = new int[instances.Count, instances.Count + 1]; var coverSetByPattern = new Dictionary <IEmergingPattern, HashSet <Instance> >(); foreach (var pattern in patterns) { if (pattern != null) { var currentCluster = new List <int>(); var currentCoverSet = new HashSet <Instance>(); for (int i = 0; i < instances.Count; i++) { if (pattern.IsMatch(instances[i])) { currentCluster.Add(i); currentCoverSet.Add(instances[i]); } } for (int i = 0; i < currentCluster.Count; i++) { for (int j = 0; j < currentCluster.Count; j++) { similarityMatrix[currentCluster[i], currentCluster[j]] += 1; similarityMatrix[currentCluster[i], instances.Count] += 1; } } coverSetByPattern.Add(pattern, currentCoverSet); } } var kmeans = new KMeans() { K = ClusterCount, classFeature = classFeature, similarityMatrix = similarityMatrix, instIdx = instIdx }; var clusterList = kmeans.FindClusters(instances); var patternClusterList = new List <List <IEmergingPattern> >(); for (int i = 0; i < ClusterCount; i++) { patternClusterList.Add(new List <IEmergingPattern>()); } foreach (var pattern in patterns) { if (pattern != null) { var bestIdx = 0; var maxCoverCount = int.MinValue; pattern.Supports = new double[ClusterCount]; pattern.Counts = new double[ClusterCount]; HashSet <Instance> bestCover = null; for (int i = 0; i < ClusterCount; i++) { HashSet <Instance> currentCover = new HashSet <Instance>(coverSetByPattern[pattern].Intersect(clusterList[i])); var currentCoverCount = currentCover.Count; pattern.Counts[i] = currentCoverCount; pattern.Supports[i] = 1.0 * currentCoverCount / clusterList[i].Count; if (currentCoverCount > maxCoverCount) { maxCoverCount = currentCoverCount; bestIdx = i; bestCover = currentCover; } } coverSetByPattern[pattern] = bestCover; patternClusterList[bestIdx].Add(pattern); } } selectedPatterns = FilterPatterns(instances, patternClusterList); if (isClassPresent) { classFeature.FeatureInformation = backupFeatureInformation; classFeature.Values = backupClassValues; for (int i = 0; i < instances.Count; i++) { instances[i][classFeature] = backupClassByInstance[i]; } } return(clusterList); }