From aefb52e5b3e6a81e55dce1a22f4870b3ef0e2c69 Mon Sep 17 00:00:00 2001
From: Revanth <109272714+revanth1718@users.noreply.github.com>
Date: Sun, 9 Jun 2024 11:48:46 +0530
Subject: [PATCH] Update K-Means_Clustering.md

---
 contrib/machine-learning/K-Means_Clustering.md | 4 ++++
 1 file changed, 4 insertions(+)

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)]