The correct answer is: A. agglomerative clustering.
Agglomerative clustering is a hierarchical clustering method. It starts with each data point in its own cluster and then merges the two most similar clusters repeatedly until all data points are in one cluster.
The algorithm works by repeatedly merging the two most similar clusters until all data points are in one cluster. The similarity between two clusters is measured using a distance metric. The most common distance metric used in agglomerative clustering is the Euclidean distance.
The following are the steps involved in agglomerative clustering:
- Initialize each data point as a separate cluster.
- Calculate the distance between all pairs of clusters.
- Merge the two clusters with the smallest distance.
- Repeat steps 2 and 3 until all clusters have been merged.
Agglomerative clustering is a powerful tool for data analysis. It can be used to cluster data points into groups based on their similarity. Agglomerative clustering is often used in machine learning and data mining applications.
Here is a brief explanation of each option:
- A. Agglomerative clustering is a hierarchical clustering method. It starts with each data point in its own cluster and then merges the two most similar clusters repeatedly until all data points are in one cluster.
- B. Expectation maximization is an iterative algorithm that is used to estimate the parameters of a statistical model. It is often used in machine learning and data mining applications.
- C. Conceptual clustering is a type of clustering that is based on the concept of similarity. It is often used in natural language processing applications.
- D. K-means clustering is a type of clustering that is based on the idea of partitioning data points into groups such that the sum of the squared distances between each data point and the centroid of its cluster is minimized.