What is k-means algorithm? [ 4 marks ]b)  Describe K-means algorithm [3 marks ].c) What are strengths and weaknesses of K-means algorithm [3 marks]?

a) The k-means algorithm is a type of unsupervised machine learning algorithm used for clustering. It is called 'k-means' because it finds 'k' number of centroids or means in a dataset and groups the data points around these centroids. Each data point is assigned to the cluster with the nearest mean. The 'k' in k-means represents the number of clusters. [4 marks]

b) The K-means algorithm works in the following way:

1. Initialize 'k' centroids randomly: These 'k' centroids are initialized randomly at the start of the algorithm.
2. Assign each data point to the nearest centroid: The distance between each data point and the 'k' centroids is calculated and each data point is assigned to the cluster with the nearest centroid.
3. Recalculate the centroids: The centroids are recalculated as the mean of all data points in the cluster.
4. Repeat steps 2 and 3: Steps 2 and 3 are repeated until the centroids do not change significantly, indicating that the algorithm has converged. [3 marks]

c) Strengths of K-means algorithm:

1. It is simple and easy to implement.
2. It is efficient in terms of computational cost, typically O(Knd), where n is the number of data points, K is the number of clusters, and d is the number of attributes.
3. It works well with large datasets.

Weaknesses of K-means algorithm:

1. The number of clusters 'k' has to be specified beforehand, which is not always feasible.
2. It is sensitive to the initial placement of centroids. Different initial placements can lead to different clusters.
3. It may not work well with clusters of different sizes and densities. [3 marks]