A threat hunter is analyzing incoming emails during the past 30 days, looking for spam...

The description outlines a process where similar data points (emails with similar attributes) are grouped based on their proximity in a multidimensional space (sender, recipient, subject, URLs, attachments), which is a classic definition ofclustering. Clustering algorithms group data points into clusters so that points in the same cluster are more similar to each other than to those in other clusters.

Clusteringis used extensively in threat hunting to identify campaigns or patterns that share common characteristics but may not be exactly the same, such as phishing emails with minor variations.

Least Frequency of Occurrence Analysisfocuses on rare events, which is opposite of the described method.

Time Series Analysisanalyzes data points in temporal order, not similarity grouping.

Most Frequency of Occurrence Analysiscounts most frequent items but does not spatially group them by similarity.

TheSplunk App for Data Science and Deep Learningsupports clustering techniques such as k-means, DBSCAN, and hierarchical clustering, which analysts can use to detect campaigns spreading across multiple users.