The correct answer is D. all of the mentioned.
K-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The algorithm requires three parameters:
- The number of clusters, k.
- A distance metric to measure the distance between two data points.
- An initial guess as to the cluster centroids.
The algorithm works by repeatedly assigning each data point to the cluster with the nearest centroid, and then updating the centroids to be the mean of the points in each cluster. This process is repeated until the centroids no longer change.
K-means clustering is a simple and effective method of clustering data. It is often used in machine learning applications such as data mining and pattern recognition.
Here is a brief explanation of each option:
- A defined distance metric is required to measure the distance between two data points. The most common distance metrics are the Euclidean distance and the Manhattan distance.
- The number of clusters is the number of groups that the data points will be partitioned into. The number of clusters is a user-defined parameter.
- An initial guess as to cluster centroids is required to start the clustering process. The centroids can be chosen randomly or based on some other heuristic.
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