diff --git a/README.md b/README.md
index 0a36e0c..a2d4935 100644
--- a/README.md
+++ b/README.md
@@ -7,8 +7,60 @@ This is the source code employed to compute DBCV (Density-Based Clustering Valid
 
 You can read the paper here: https://epubs.siam.org/doi/10.1137/1.9781611973440.96
 
-The Matlab source code from this repository is from Davoud Moulavi and Pablo A. Jaskowiak.
+The Matlab source code from this repository is from [Davoud Moulavi](https://scholar.google.com.br/citations?user=VoJmF8QAAAAJ) and [Pablo A. Jaskowiak](https://scholar.google.com.br/citations?user=by6xRbkAAAAJ). 
+
+## Usage
+
+For calculating the DBCV Validation Index of a partition run dbcv.m. The output of the function is the DBCV index for the corresponding partition. Its values are within -1 and +1. DBCV is a *maximization* index, which means that higher values correspond to better partitions, according to the index. The necessary inputs are the dataset (without labels) and the partition, which correspond to a clustering solution, that is, labels. The dataset should be formatted with each line corresponding to an object, with columns corresponding to features. The partition is a 1-dimensional array with cluster assignments, in integer format. The number of labels must match the number of objects in the dataset. 
+
+**Important note**: noise is represented by label 0 (zero). Therefore, any object with label of zero in the partition is considered as noise. By definition, singletons (clusters with a single object) will be treated as noise by the measure (please see the paper for discussion on this topic).
+
+
+### Example: Computing DBCV on a Synthetic Dataset
+
+Assuming you are running Matlab and your current working directory is *src*.
+
+```
+%first we load the dataset
+load ../data/dataset_1.txt
+
+%we run dbcv on the dataset (first two columns),
+%passing its ground truth as partition (column 3).
+val = dbcv(dataset_1(:,-3),dataset_1(:,3))
+
+%val should be 0.6149. Note that this is low given
+%that some noise points actually overlap the clusters.
+
+%we can also plot the dataset with the following
+plot_clusters(dataset_1(:,1:2),dataset_1(:,3))
+
+%that's all :)
+```
+
+
+## Datasets
+
+In the folder *data* you can find the four synthetic datasets employed in our paper. These are shown below. Plots in PDF and PNG can be found in the folder *plots*. Please, if you use any of these datasets in a publication, cite *both* our paper and this repository.
+
+<p align="center">
+<img src="./plots/dataset_1.png" width="200" />
+<img src="./plots/dataset_2.png" width="200" />
+</p>
+
+<p align="center">
+<img src="./plots/dataset_3.png" width="200" />
+<img src="./plots/dataset_4.png" width="200" />
+</p>
+
+
+## Other resources
 
 You can also find an implementation of DBCV in the R Package [clusterConfusion](https://github.com/pajaskowiak/clusterConfusion).
 
-If you find any bugs or problems, please, get in touch.
+## Disclaimer 
+
+This prototype was developed for research purposes only. It was *by no means* implemented to optimize computational performance. Therefore, its computational performance should not be evaluated and/or fairly compared to that of other measures.
+
+## Contact
+
+If you find any bugs or problems, please, get in touch. You can reach me [here](https://andretta.paginas.ufsc.br/).
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new file mode 100644
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--- /dev/null
+++ b/data/dataset_1.txt
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diff --git a/data/dataset_3.txt b/data/dataset_3.txt
new file mode 100644
index 0000000..09609a5
--- /dev/null
+++ b/data/dataset_3.txt
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\ No newline at end of file
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diff --git a/src/MST_Edges.m b/src/MST_Edges.m
new file mode 100644
index 0000000..8d95af7
--- /dev/null
+++ b/src/MST_Edges.m
@@ -0,0 +1,52 @@
+function [Edg Degr] = MST_Edges(G, start,G_edges_weights)
+
+% G_edges_weights; % All-to-all mutual reachability distances, declared as global in the main program
+
+% intree (1 x G.no_vertices) = Is the vertex in the tree ?
+% d (1 x G.no_vertices) = "Distance" vertex is from partial MST
+% v = Current vertex to process
+% next_v = Candidate next vertex
+% weight = Edge weight
+% dist = Best current "distance" from partial MST
+% G.MST_parent (G.no_vertices x 1) = MST discovery hierarchy
+% G.MST_edges (G.no_vertices x 3) = MST edges (a pair of vertices and the corresponding weight)
+% G.MST_degrees (No x 1) = MST vertex degrees (a self-edge counts one additional degree !)
+
+%'COMPUTING THE MST (PRIMs Algorithm on a Complete Graph using List Search)';
+
+for i=1:G.no_vertices
+	intree(i) = 0;
+	d(i) = Inf;
+	G.MST_parent(i) = i;
+end
+
+d(start) = 0;
+v = start;
+counter = 0;
+
+while (counter < (G.no_vertices - 1))
+	intree(v) = 1;
+	dist = Inf;
+	for w=1:G.no_vertices
+		if ((w~=v) && (intree(w) == 0))
+			weight = G_edges_weights(v,w);
+			if (d(w) > weight)
+				d(w) = weight;
+				G.MST_parent(w) = v;
+			end
+			if (dist > d(w))
+				dist = d(w);
+				next_v = w;
+			end
+		end
+
+	end
+	counter = counter + 1;
+	G.MST_edges(counter,:) = [G.MST_parent(next_v)  ;  next_v  ;  G_edges_weights(G.MST_parent(next_v) , next_v)];	% Includes Edge into the MST
+	G.MST_degrees(G.MST_parent(next_v)) = G.MST_degrees(G.MST_parent(next_v)) + 1;			% Increments MST vertex degree
+	G.MST_degrees(next_v) = G.MST_degrees(next_v) + 1;						% Increments MST vertex degree
+	v  = next_v;										% Vertex included
+end
+Edg = G.MST_edges;
+Degr = G.MST_degrees;
+
diff --git a/src/dbcv.m b/src/dbcv.m
new file mode 100644
index 0000000..b00a1b6
--- /dev/null
+++ b/src/dbcv.m
@@ -0,0 +1,93 @@
+function valid = dbcv(data,partition)
+
+clusters  = unique(partition);
+dist      = squareform(pdist(data)).^2;
+%treating singleton clusters
+for i=1:length(clusters)
+    if(sum(partition == clusters(i)) == 1)
+        partition(partition == clusters(i)) = 0;
+        clusters(i) = 0;
+    end
+end
+
+%clusters without noise and singletons
+clusters  = setdiff(clusters,0);
+
+if (isempty(clusters) || (length(clusters) == 1))
+    valid = 0;
+    return;
+end
+
+data      = data(partition~=0,:);
+
+dist      = dist(partition~=0,partition~=0);
+
+poriginal = partition;
+partition = partition(partition~=0);
+
+
+nclusters = length(clusters);
+
+[nobjects nfeatures] = size(data);
+
+d_ucore_cl = zeros(1,nobjects);
+compcl     = zeros(1,nclusters);
+int_edges  = cell(1,nclusters);
+int_node_data   = cell(1,nclusters);
+for i=1:nclusters
+    
+    objcl  = find(partition == clusters(i));
+    
+    nuobjcl = length(objcl);
+            
+    [d_ucore_cl(objcl) mr] = matrix_mutual_reachability_distance(nuobjcl, dist(objcl,objcl),nfeatures);  % ucore distance of each object in its own cluster
+     
+    G.no_vertices = nuobjcl;
+    G.MST_edges   = zeros(nuobjcl-1,3);
+    G.MST_degrees = zeros(nuobjcl,1);
+    G.MST_parent  = zeros(nuobjcl,1);
+    
+    [Edges Degrees] = MST_Edges(G, 1,mr);
+    
+    int_node     = find(Degrees~=1);
+    int_edg1     = find(ismember(Edges(:,1),int_node));    
+    int_edg2     = find(ismember(Edges(:,2),int_node));    
+    int_edges{i} = intersect(int_edg1,int_edg2);
+    
+    
+    if (~isempty(int_edges{i}))
+        compcl(i) = max(Edges(int_edges{i},3));
+    else 
+        compcl(i) = max(Edges(:,3));         
+    end
+    int_node_data{i} = objcl(int_node);
+    if isempty(int_node_data{i})
+        int_node_data{i} = objcl;
+    end
+end
+
+sep_point = zeros(nobjects,nobjects);
+for i=1:(nobjects-1)
+    for j=i:nobjects
+        sep_point(i,j) = max([dist(i,j) d_ucore_cl(i) d_ucore_cl(j)]);        
+        sep_point(j,i) = sep_point(i,j);
+    end
+end
+
+valid = 0;
+sepcl = Inf(nclusters,1);
+for i=1:nclusters
+    other_cls = setdiff(clusters,clusters(i)); 
+    
+    sep = zeros(1,length(other_cls));
+    for j=1:length(other_cls)                
+        sep(j) = min(min(sep_point(int_node_data{i},int_node_data{clusters==other_cls(j)}))); 
+    end
+    sepcl(i) = min(sep);
+    dbcvcl = (sepcl(i) - compcl(i)) / max(compcl(i),sepcl(i));
+    valid = valid + (dbcvcl * sum(partition == clusters(i)));
+end
+
+valid = valid / length(poriginal); 
+
+end    
diff --git a/src/matrix_mutual_reachability_distance.m b/src/matrix_mutual_reachability_distance.m
new file mode 100644
index 0000000..7d839fb
--- /dev/null
+++ b/src/matrix_mutual_reachability_distance.m
@@ -0,0 +1,23 @@
+function [d_ucore G_edges_weights] = matrix_mutual_reachability_distance(MinPts, G_edges_weights,d)
+
+No = size(G_edges_weights,1);
+
+K_NN_Dist = G_edges_weights.^(-1*d);	
+K_NN_Dist(K_NN_Dist == Inf) = 0;
+
+%original
+d_ucore         = sum(K_NN_Dist);
+
+d_ucore         = d_ucore / (No - 1);    
+d_ucore         = (1./d_ucore).^(1/(1*d));
+d_ucore(d_ucore == Inf) = 0;
+    
+for i = 1:No
+    for j=1:MinPts        
+        G_edges_weights(i,j) = max([d_ucore(i)  d_ucore(j)  G_edges_weights(i,j)]);
+        G_edges_weights(j,i) = G_edges_weights(i,j); 
+    end
+end
+
+end
+
diff --git a/src/plot_clusters.m b/src/plot_clusters.m
new file mode 100644
index 0000000..90085e5
--- /dev/null
+++ b/src/plot_clusters.m
@@ -0,0 +1,15 @@
+function plot_clusters(data,labels)
+
+color = {'b','g','r','c','m','y'};
+point = {'x','o','*','s','d','v','^','>','<','p','h'};
+
+for i=1:size(data,1)
+    if ((labels(i) == -1) || sum(labels == labels(i)) == 1 )
+         plot(data(i,1),data(i,2),'k+','MarkerSize',6);
+    else
+        plot(data(i,1),data(i,2),strcat(color{mod(labels(i),length(color))+1},point{mod(labels(i),length(point))+1}),'MarkerSize',9);
+    end
+    hold on;
+end
+
+end