Updated

Hierarchical_Clustering_Guide.asv

@@ -0,0 +1,67 @@
+%%%------------- Hierarchical Clustering
+% Step 1: Consider each data point as a single cluster.
+% Step 2: Combine the two closest clusters and make them one cluster.
+% Step 3: Repeatedly combine clusters until there is only one cluster.
+
+% Approaches for finding closest clusters
+% Single Link: Min distance
+% Complete Link: Max distance
+% Average: Average distance
+
+% Types of Hierachical Clustering : Agglomerative, Divisive
+
+% Import the dataset
+data = readtable('Datasets\Mall_Customers.csv');
+
+%Check for missing values
+missings = sum(ismissing(data));
+
+%Plot variables to check for outliers
+IncomePlot = plot(data.AnnualIncome);
+SpendingPlot = plot(data.SpendingScore);
+
+% Perform Feature Scaling (Standardization Method)
+stand_income = (data.AnnualIncome - mean(data.AnnualIncome)) / std(data.AnnualIncome);
+data.AnnualIncome = stand_income; 
+
+stand_spending = (data.SpendingScore - mean(data.SpendingScore)) / std(data.SpendingScore);
+data.SpendingScore = stand_spending; 
+
+% Select columns for clustering
+selected_data = data(:,4:5);
+
+%Data must be an array to be used in clustering algorithm
+arrayed_data = table2array(selected_data);
+
+% Select linkage method
+z = linkage(arrayed_data, 'ward');
+
+% Create dendogram
+dendrogram(z);
+
+% Determining tresholds for optimal number of clusters using links
+% inconsistencies
+i = inconsistent(z,7); % 7 is the deepest link depth
+% i produces 4 column 
+% Col1 represents average height of all links in calculation
+% Col2 represents standard deviation of all links in calculation
+% Col3 represents number of links included in calculation
+% Col4 represents inconsistency score
+[a,b]= max(i(:,4)); % max inconsistency score
+
+
+%------- Visualization
+% data = arrayed_data;
+% figure,
+% 
+% gscatter(data(:,1),data(:,2),idx);
+% hold on
+% 
+% for i=1:6
+%     scatter(C(i,1),C(i,2),96,'black','filled');
+% end
+% 
+% legend({'Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5','Cluster 6' })
+% xlabel('Annual Income');
+% ylabel('Spending Score');
+% hold off

Hierarchical_Clustering_Guide.m

@@ -33,20 +33,40 @@
 %Data must be an array to be used in clustering algorithm
 arrayed_data = table2array(selected_data);
 
+% Select linkage method
+z = linkage(arrayed_data, 'ward');
 
+% Create dendogram
+dendrogram(z);
 
-%------- Visualization
-data = arrayed_data;
-figure,
+% Determining tresholds for optimal number of clusters using links
+% inconsistencies
+i = inconsistent(z,7); % 7 is the deepest link depth
+% i produces 4 column 
+% Col1 represents average height of all links in calculation
+% Col2 represents standard deviation of all links in calculation
+% Col3 represents number of links included in calculation
+% Col4 represents inconsistency score
+[a,b]= max(i(:,4)); % max inconsistency score and save it in a and its index in b
 
-gscatter(data(:,1),data(:,2),idx);
-hold on
+% Perform clustering
+% Set treshold as little less than the max inconsistent element.With cutoff
+% method (heights)
+C= cluster(z,'cutoff', z(b,3)-0.1, 'Criterion', 'distance');
 
-for i=1:6
-    scatter(C(i,1),C(i,2),96,'black','filled');
-end
 
-legend({'Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5','Cluster 6' })
-xlabel('Annual Income');
-ylabel('Spending Score');
-hold off
+%------- Visualization
+% data = arrayed_data;
+% figure,
+% 
+% gscatter(data(:,1),data(:,2),idx);
+% hold on
+% 
+% for i=1:6
+%     scatter(C(i,1),C(i,2),96,'black','filled');
+% end
+% 
+% legend({'Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5','Cluster 6' })
+% xlabel('Annual Income');
+% ylabel('Spending Score');
+% hold off