Principles of Food Science
Divisive clustering is a hierarchical clustering technique that starts with all data points in a single cluster and iteratively splits the cluster into smaller sub-clusters. This method contrasts with agglomerative clustering, which begins with individual points and merges them into larger clusters. Divisive clustering is particularly useful for identifying distinct groups within complex datasets, allowing for a more nuanced understanding of sensory data by uncovering underlying patterns.
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