BUAP: performance of K-Star at the INEX'09 clustering task
2009
The aim of this paper is to use unsupervised classification techniques in order to group the documents of a given huge collection into clusters. We approached this challenge by using a simple clustering algorithm (K-Star) in a recursive clustering process over subsets of the complete collection.
The presented approach is a scalable algorithm which may automatically discover the number of clusters. The obtained results outperformed different baselines presented in the INEX 2009 clustering task.
Keywords:
- Single-linkage clustering
- k-medians clustering
- Correlation clustering
- Fuzzy clustering
- Cluster analysis
- FLAME clustering
- Canopy clustering algorithm
- Machine learning
- Artificial intelligence
- CURE data clustering algorithm
- Computer science
- Pattern recognition
- Determining the number of clusters in a data set
- Data stream clustering
- Data mining
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