diff --git a/course_files/clust-intro.Rmd b/course_files/clust-intro.Rmd
index f91ed434b669302489dfb9cd2bf0ca01af7e3971..12e5ac5d24905c66b7763fd00c9d70b00d3fe11d 100644
--- a/course_files/clust-intro.Rmd
+++ b/course_files/clust-intro.Rmd
@@ -209,7 +209,7 @@ that are very fast, although not the most accurate approaches.
    
   
 
-####  Concensus clustering (more robustness, less computational speed)
+####  Consensus clustering (more robustness, less computational speed)
 
 ##### __Motivation (Two problems of $K$-means)__: \
 - __Problem1:__  sensitive to initial partitions \
@@ -219,7 +219,7 @@ that are very fast, although not the most accurate approaches.
    __Solution:__ 
     Run $K$-means with a range of $K$'s.
 
-##### __Algorithm of concensus clustering (simpliest version)__: 
+##### __Algorithm of consensus clustering (simpliest version)__: 
 ```{r, eval = F, highlight = F}
 for(k in the range of K){
   for(each subsample of the data){
@@ -252,7 +252,7 @@ Say we partitioned four data points into 2 clusters.
 
 <center>![](figures/concensus1.png){width=60%}</center>
 
-- __Step2:__ Concensus matrix: \
+- __Step2:__ Consensus matrix: \
  Average of all the partitions
  <center>![](figures/concensus2.png){width=30%}</center>