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

• Type: Journal
• Year: 2010
• Where: SOCIALCOM
• Authors: Mark K. Goldberg, Mykola Hayvanovych, Malik Magdon-Ismail
• Keywords: Algorithms, Security Privacy, Social Groups, Social Network, SOCIALCOM 2010