diff --git a/contrib/machine-learning/K-Means_Clustering.md b/contrib/machine-learning/K-Means_Clustering.md
index 859636b..83fbd80 100644
--- a/contrib/machine-learning/K-Means_Clustering.md
+++ b/contrib/machine-learning/K-Means_Clustering.md
@@ -76,6 +76,10 @@ The K-means algorithm follows an iterative approach to optimize cluster formatio
  Predicted cluster for new data: [0]
 ## Conclusion
 **K-Means** can be applied to data that has a smaller number of dimensions, is numeric, and is continuous or can be used to find groups that have not been explicitly labeled in the data. As an example, it can be used for Document Classification, Delivery Store Optimization, or Customer Segmentation.
+## Reference 
+[[Survey of Machine Learning and Data Mining Techniques used in Multimedia System](https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)]
+
+[[A Clustering Approach for Outliers Detection in a Big Point-of-Sales Database](https://www.researchgate.net/publication/339267868_A_Clustering_Approach_for_Outliers_Detection_in_a_Big_Point-of-Sales_Database?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6Il9kaXJlY3QiLCJwYWdlIjoiX2RpcmVjdCJ9fQ)]