Identifying robust clusters and multi-community nodes by combining top-down and bottom-up approaches to clustering.
Chris GaiteriMingming ChenBoleslaw K. SzymanskiKonstantin KuzminJierui XieChangkyu LeeTimothy BlancheElias Chaibub NetoSu-Chun HuangThomas J. GrabowskiTara M. MadhyasthaVitalina KomashkoPublished in: CoRR (2015)
Keyphrases
- clustering algorithm
- clustering approaches
- hierarchical clustering
- self organizing maps
- k means
- clustering method
- fuzzy clustering
- overlapping clusters
- data clustering
- data points
- unsupervised clustering
- heterogeneous information networks
- clustering procedure
- subspace clustering
- arbitrary shape
- cluster analysis
- unsupervised learning
- information networks
- document clustering
- shortest path
- fuzzy c means
- cluster centers
- similarity matrix
- clustering scheme
- data objects
- agglomerative hierarchical clustering
- agglomerative clustering
- clustering framework
- parameter free
- graph structure
- spectral clustering