Our Office
Ernakulam, Kottayam
Email Us
linusfacts@gmail.com
Call Us
+91 9544409513
What is the primary goal of outlier detection algorithms in unsupervised learning? What is a common challenge when applying clustering algorithms to high-dimensional data? What is a common application of clustering algorithms in unsupervised learning?

QUESTIONS

What is the primary goal of outlier detection algorithms in unsupervised learning?

Right Answer:

The primary goal of outlier detection algorithms in unsupervised learning is to identify unusual or unexpected data points, which may be indicative of errors, anomalies, or interesting phenomena.

What is a common challenge when applying clustering algorithms to high-dimensional data?

Right Answer:

The curse of dimensionality is a common challenge when applying clustering algorithms to high-dimensional data, as the distance between data points becomes less meaningful and the data becomes sparse, making it difficult to find meaningful clusters.

What is a common application of clustering algorithms in unsupervised learning?

Right Answer:

Customer segmentation
Customer segmentation is a common application of clustering algorithms in unsupervised learning, as it involves grouping customers based on their behavior, preferences, or demographic characteristics to inform marketing strategies and business decisions.

Get In Touch

Kochi, Pala,Ernakulam

+91 9544409513

linuslearning.in@gmail.com

Our Courses
Newsletter

Those people who develop the ability to continuously acquire new and better forms of knowledge that they can apply to their work and to their lives will be the movers and shakers in our society for the indefinite future