Short communication: Variable space hidden Markov model for topic detection and analysis
Knowledge-Based Systems
Multi-grain hierarchical topic extraction algorithm for text mining
Expert Systems with Applications: An International Journal
Dynamic hierarchical algorithms for document clustering
Pattern Recognition Letters
Semantic multi-grain mixture topic model for text analysis
Expert Systems with Applications: An International Journal
Topics modeling based on selective Zipf distribution
Expert Systems with Applications: An International Journal
Latency based group discovery algorithm for network aware cloud scheduling
Future Generation Computer Systems
Hi-index | 0.00 |
This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the â-similarity graph, and a cover routine. We also describe two methods obtained from this framework called Hierarchical Compact Algorithm and Hierarchical Star Algorithm. These algorithms have been evaluated using standard document collections. The experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality.