Learn why clustering is the best type of machine learning model to decide the number and location of emergency units for a hospital care chain. Clustering can group accident-prone areas and place the units near the cluster centers.
A Hospital Care chain wants to open a series of Emergency-Care wards within a region. The chain knows the location of all the maximum accident-prone areas in the region. They have to decide the number of the Emergency Units to be opened and the location of these Emergency Units, so that all the accident-prone areas are covered in the vicinity of these Emergency Units. Which type of machine learning model is best to be applied in this scenario?
The best type of machine learning model to be applied in this scenario is clustering. Clustering is a type of unsupervised learning that groups data points based on their similarity or proximity. Clustering can help to identify the optimal number and location of the emergency units by finding the clusters of accident-prone areas and placing the units near the cluster centers. This way, the emergency units can cover the maximum area and minimize the distance to the accident sites.
Some examples of clustering algorithms are K-means, DBSCAN, and hierarchical clustering. K-means partitions the data into K clusters by minimizing the sum of squared distances from each point to the cluster center. DBSCAN finds clusters of high density and separates them from low-density regions. Hierarchical clustering builds a tree-like structure of clusters by either merging smaller clusters into larger ones (agglomerative) or splitting larger clusters into smaller ones (divisive).
Clustering is different from regression and classification, which are types of supervised learning. Regression predicts a continuous output value based on the input features, such as predicting the house price based on the size, location, and amenities. Classification predicts a discrete output label based on the input features, such as predicting the sentiment of a text review as positive or negative.
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