Final answer:
Option B). Unsupervised Learning is the data analysis method that deals with detecting patterns in unlabeled data. It contrasts with supervised learning, which utilizes labeled data, and is particularly useful for identifying clustered or dispersed patterns in different phenomena, which can lead to insights about underlying causes and potential solutions.
Step-by-step explanation:
Unsupervised learning is the challenge of identifying abstracted patterns or structures in unlabeled data. Through the analysis and clustering of unlabeled datasets, this machine learning technique finds hidden patterns or data groupings without requiring human intervention. Unsupervised learning algorithms attempt to make sense of and draw conclusions from datasets without reference to known or labelled outcomes, in contrast to supervised learning where models are trained on labelled data.
In terms of pattern recognition, we look at the outcomes of the clustered patterns when examining mapped phenomena, such as the location of payday lenders in the San Fernando Valley of Los Angeles. These patterns indicate that the phenomena, which in this case are payday lending establishments, are not distributed randomly but rather are impacted by particular processes. Finding these trends aids in figuring out the root causes and developing remedies. Comparably, highly dispersed patterns indicate that an active process—like fire stations or convenience stores—is probably producing a repulsive effect between points.