<prompt> Akaretsa likarolo tsa bohlokoa tsa K-means Clustering Algorithmnan  <response>: Instruction in English: Summarise the key features of a K-means Clustering Algorithm . Response in English: The K-means clustering algorithm is an unsupervised machine learning method used to partition data into K clusters. The key features of this algorithm are:1. Centroid initialization: The algorithm begins by randomly choosing K initial centroids, where K is the number of clusters desired.2. Assignment of points to clusters: Each data point is then assigned to the nearest centroid based on a distance metric, and each collection of points assigned to a centroid forms a cluster.3. Recalculation of centroids: The centroids are then recalculated as the mean of all points within the clusters.4. Iterative process: Steps 2 and 3 are repeated until the assignment of points to clusters no longer changes or a maximum number of iterations is reached.5. Results: The algorithm produces K clusters of data, where the sum of the distances between each data point and the centroid of its assigned cluster is minimized.The K-means algorithm is efficient and easy to implement, but can be sensitive to the initial centroid selection and can get stuck in local minima. There are variations of the K-means algorithm that address these limitations. . Response in Sesotho: K-means clustering algorithm ke mokhoa o sa laoleheng oa ho ithuta oa mochini o sebelisetsoang ho arola data ka lihlopha tsa K. Likarolo tsa bohlokoa tsa algorithm ena ke: 1. Qalo ea Centroid: Algorithm e qala ka ho khetha ka mokhoa o sa reroang li-centroid tsa K, moo K e leng palo ea lihlopha tse lakatsehang. 2. Kabelo ea lintlha ho lihlopha: Sebaka ka seng sa data se abeloa centroid e haufinyane ho latela metric ea bohole, me pokello e ngoe le e ngoe ea lintlha tse abetsoeng centroid e etsa sehlopha. 3. Ho baloa bocha ha li-centroids: Joale li-centroid li baloa bocha e le moelelo oa lintlha tsohle ka hara lihlopha. 4. Mokhoa oa ho pheta-pheta: Mehato ea 2 le ea 3 e phetoa ho fihlela kabo ea lintlha ho lihlopha e se e sa fetohe kapa palo e kholo ea ho pheta-pheta e fihletsoe. 5. Liphetho: Algorithm e hlahisa lihlopha tsa K tsa data, moo kakaretso ea libaka tse pakeng tsa ntlha e ngoe le e ngoe ea data le centroid ea sehlopha seo e se abetsoeng e fokotsoang. K-means algorithm e sebetsa hantle ebile e bonolo ho e sebelisa, empa e ka ba le maikutlo a matle ho khetho ea pele ea centroid mme e ka tsoareha ho minima ea lehae. Ho na le mefuta e fapaneng ea algorithm ea K-means e sebetsanang le mefokolo ena. 