BUAP: Performance of K-Star at the INEX’09 Clustering Task
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.
KeywordsGround Truth Similarity Matrix Vector Space Model Inverse Document Frequency Cluster Evaluation
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