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SOCIALCOM
2010

Measuring Similarity between Sets of Overlapping Clusters

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Measuring Similarity between Sets of Overlapping Clusters
The typical task of unsupervised learning is to organize data, for example into clusters, typically disjoint clusters (eg. the K-means algorithm). One would expect (for example) a clustering of books into topics to present overlapping clusters. The situation is even more so in social networks, a source of ever increasing data. Finding the groups or communities in social networks based on interactions between individuals (a measure of similarity) is an unsupervised learning task; and, groups overlap
Mark K. Goldberg, Mykola Hayvanovych, Malik Magdon
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where SOCIALCOM
Authors Mark K. Goldberg, Mykola Hayvanovych, Malik Magdon-Ismail
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