lecture12-clustering-handout-6-per

In order to maintain an overall on2 performance

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Unformatted text preview: omplete ­link   Similarity of the furthest points, the least cosine ­similar   Centroid   Clusters whose centroids (centers of gravity) are the most cosine ­similar   Average ­link Note: the resulting clusters are still hard and induce a partition Introduc)on to Informa)on Retrieval Sec. 17.2 Single Link Agglomera*ve Clustering   Average cosine between pairs of elements Introduc)on to Informa)on Retrieval Sec. 17.2 Single Link Example   Use maximum similarity of pairs: sim(ci ,c j ) = max sim( x, y) x∈ci , y∈c j   Can result in straggly (long and thin) clusters due to chaining effect.   Ager merging ci and cj, the similarity of the resul*ng cluster to another cluster, ck, is: sim(( ci ∪ c j ), ck ) = max( sim(ci , ck ), sim(c j , ck )) 6 Introduc)on to Informa)on Retrieval ec. 17.2 S Complete Link Introduc)on to Informa)on Retrieval Sec. 17.2 Comp...
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