Skip to Content

AI-900: Clustering: The Best Machine Learning Model for Emergency Unit Location

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?

A. Clustering
B. Regression
C. Classification


A. Clustering

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-meansDBSCAN, 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.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

    Ads Blocker Image Powered by Code Help Pro

    Your Support Matters...

    We run an independent site that is committed to delivering valuable content, but it comes with its challenges. Many of our readers use ad blockers, causing our advertising revenue to decline. Unlike some websites, we have not implemented paywalls to restrict access. Your support can make a significant difference. If you find this website useful and choose to support us, it would greatly secure our future. We appreciate your help. If you are currently using an ad blocker, please consider disabling it for our site. Thank you for your understanding and support.