Update README.md

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+## MeanShift Clustering
+
 Mean shift clustering is a sliding-window-based algorithm that attempts to find dense areas of data points. It is a centroid-based algorithm meaning that the goal is to locate the center points of each group/class, which works by updating candidates for center points to be the mean of the points within the sliding-window. These candidate windows are then filtered in a post-processing stage to eliminate near-duplicates, forming the final set of center points and their corresponding groups.
 
 In contrast to K-means clustering there is no need to select the number of clusters as mean-shift automatically discovers this. That’s a massive advantage. The fact that the cluster centers converge towards the points of maximum density is also quite desirable as it is quite intuitive to understand and fits well in a naturally data-driven sense. 
 
 The drawback is that the selection of the window size/radius “r” can be non-trivial.
 
 More on Clustering: https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68
+
+### Generated Data
+![](MeanShift/results/0*generated_data.jpg)
+
+### Actual Cluster Centers
+![](MeanShift/results/2*cluster_centers.jpg)
+
+### Predicting Cluster Centers
+
+* #### Initialization of Predicted Cluster Centers
+  ![](MeanShift/results/1*progress_0000_initialization_of_cluster_centers.jpg)
+
+* #### Predicted Cluster Centers after 2nd Iteration
+  ![](MeanShift/results/1*progress_0002_cluster_centers.jpg)
+
+* #### Predicted Cluster Centers after 4th Iteration
+  ![](MeanShift/results/1*progress_0004_cluster_centers.jpg)
+
+* #### Predicted Cluster Centers after 6th Iteration
+  ![](MeanShift/results/1*progress_0006_cluster_centers.jpg)
+
+### Final Mean Shift Clustering Result
+![](MeanShift/results/3*meanShift_result.jpg)