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.
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.
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.
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.