The correct answer is: A. k-means clustering
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 specifying k, which is the number of desired clusters. The algorithm then proceeds by randomly assigning each observation to one of the k clusters. The mean of each cluster is then computed. Observations are then reassigned to the cluster with the nearest mean. This process is repeated until the assignments do not change.
K-means clustering is a simple and efficient algorithm that is often used for data analysis. However, it can be sensitive to the initial choice of k.
Here is a brief explanation of each option:
- A. 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.
- B. conceptual clustering is a type of machine learning that groups objects based on their similarities. It is a supervised learning algorithm, which means that it requires a set of labeled data to train on. Conceptual clustering algorithms work by finding a set of prototypes, or concepts, that can be used to represent the data. The objects are then grouped into clusters based on their similarity to the concepts.
- C. expectation maximization is an iterative algorithm for estimating the parameters of a statistical model. It is often used in machine learning for tasks such as clustering and classification. The algorithm works by alternating between two steps: expectation and maximization. In the expectation step, the algorithm calculates the expected value of the parameters given the data. In the maximization step, the algorithm maximizes the likelihood of the data given the estimated parameters.
- D. agglomerative clustering is a hierarchical clustering algorithm that starts with each observation in its own cluster and then merges clusters until there is only one cluster left. The algorithm starts by calculating the distance between each pair of observations. The two observations with the smallest distance are then merged into a single cluster. This process is repeated until all observations are in a single cluster.
I hope this helps!