<prompt> Kopanya lintlha tse latelang tsa data ka lihlopha tse peli u sebelisa algorithm ea ho kopanya ea K-Means.65, 55, 45, 35, 25, 15  <response>: Instruction in English: Group the following data points into two clusters using the K-Means clustering algorithm. . Response in English: First, to perform the K-means clustering on this dataset, lets initialize the value of K to 2, as weve been instructed to create two clusters. Our objective is to partition these 2D points into 2 clusters so that the points in a cluster are as close to the cluster center as possible.Step 1: Choose 2 random points from the dataset to be the initial centroids c1 and c2. In this example, we take the minimum and maximum values as the initial centroids. So, Initially, c1 is 15 and c2 is 65.Step 2: Calculate the distance of all other points from these two centroids. For the point 55, the distance from c1 is 40 and from c2 is 10. Since 55 is closer to c2, it is assigned to the cluster of centroid c2. Similarly, we find the distances of other points and assign them to either cluster 1 or cluster 2. Point Distance from c1 Distance from c2 Assigned Cluster ------------ 55 40 10 c2 45 30 20 c2 35 20 30 c1 25 10 40 c1 15 0 50 c1 So, Cluster 1 has points 35,25,15 and Cluster 2 has points 65,55,45Step 3: Find the average of all the points in the cluster and update the new centroid. New c1 average of 35,25,15 25New c2 average of 65,55,45 55Repeat step 2 and step 3 until the centroids dont change.After repeating the steps, the centroids wont change anymore and we are left with the following two clusters:Cluster 1: 35,25,15 with centroid c1: 25Cluster 2: 65,55,45 with centroid c2: 55So, we have successfully used the K-means clustering algorithm to group the data points into two clusters . Response in Sesotho: Taba ea pele, ho kopanya K-means ho datha ena, ha re qaleng boleng ba K ho isa ho 2, joalo ka ha re laetsoe ho theha lihlopha tse peli. Sepheo sa rona ke ho arola lintlha tsena tsa 2D ka lihlopha tse 2 e le hore lintlha tse ka hara sehlopha li be haufi le setsi sa lihlopha ka hohle kamoo ho ka khonehang. Mohato oa 1: Khetha lintlha tse 2 tse sa reroang ho tsoa pokellong ea data ho ba li-centroids tsa pele tsa c1 le c2. Mohlaleng ona, re nka bonyane le boholo ba boleng joalo ka li-centroids tsa pele. Kahoo, qalong, c1 ke 15 le c2 ke 65. Mohato oa 2: Bala sebaka sa lintlha tse ling kaofela ho tloha ho li-centroid tsena tse peli. Bakeng sa ntlha 55, sebaka se tsoang ho c1 ke 40 le ho tloha ho c2 ke 10. Kaha 55 e haufi le c2, e abeloa sehlopha sa centroid c2. Ka mokhoa o tsoanang, re fumana libaka tsa lintlha tse ling ebe re li abela ho sehlopha sa 1 kapa sehlopha sa 2. Ntlha Sebaka ho tloha ho c1 Sebaka ho tloha ho c2 Sehlopha se abetsoeng --------- --- 55 40 10 c2 45 30 20 c2 35 20 30 c1 25 10 40 c1 15 0 50 c1 Kahoo, Cluster 1 e na le lintlha 35,25,15 le Cluster 2 e na le lintlha 65,55,45 Mohato oa 3: Fumana karolelano ea lintlha tsohle sehlopheng me u ntjhafatse centroid e ncha. Ncha c1 karolelano ea 35,25,15 25 Ncha c2 karolelano ea 65,55,45 55 Pheta mohato oa 2 le mohato oa 3 ho fihlela li-centroids li sa fetohe. Ka mora ho pheta mehato, li-centroids ha li sa tla hlola li fetoha me re sala le lihlopha tse peli tse latelang: Cluster 1: 35,25,15 e nang le centroid c1: 25 Cluster 2: 65,55,45 with centroid c2: 55 Kahoo, re atlehile ho sebelisa algorithm ea K-means clustering ho hlophisa lintlha tsa data ka lihlopha tse peli. 