Hierarchical clustering is often portrayed as the better
quality clustering approach, but is limited because of its quadratic
time complexity. In contrast, K-means and its variants have a
time complexity that is linear in the number of documents, but are
thought to produce inferior clusters. Sometimes K-means and
agglomerative hierarchical approaches are combined so as to “get
the best of both worlds.” For example, in the document domain,
Scatter/Gather [1], a document browsing system based on
clustering, uses a hybrid approach involving both K-means and
agglomerative hierarchical clustering. K-means is used because of
its run-time efficiency and agglomerative hierarchical clustering is
used because of its quality.
Hierarchical clustering is often portrayed as the betterquality clustering approach, but is limited because of its quadratictime complexity. In contrast, K-means and its variants have atime complexity that is linear in the number of documents, but arethought to produce inferior clusters. Sometimes K-means andagglomerative hierarchical approaches are combined so as to “getthe best of both worlds.” For example, in the document domain,Scatter/Gather [1], a document browsing system based onclustering, uses a hybrid approach involving both K-means andagglomerative hierarchical clustering. K-means is used because ofits run-time efficiency and agglomerative hierarchical clustering isused because of its quality.
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