Name
Abstract | ||
|---|---|---|
We propose an approach for discovering functional communities in social media by identifying groups of users who interact with similar content, represented as dense biclusters in a user-content matrix. We present a heuristic algorithm to efficiently search the space of possible co-clusterings for one which maximizes the value of a given metric, along with a new class of co-clustering metrics that are more suitable for this task than existing metrics. We evaluate our approach using synthetic and real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDMW.2015.92 | ICDM Workshops |
Field | DocType | Citations |
Data mining,Clustering high-dimensional data,Social media,Matrix partitioning,Correlation clustering,Computer science,Heuristic (computer science),Artificial intelligence,Biclustering,Cluster analysis,Sparse matrix,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Thompson | 1 | 8 | 8.60 |
| l ness | 2 | 0 | 0.68 |
| David Shallcross | 3 | 0 | 0.34 |
| Devasis Bassu | 4 | 22 | 65.13 |