Abstract Hierarchical clustering algorithm is applied in many research fields such as data mining and machine learning. Most existing hierarchical clustering algorithms are dependent on symmetrical distances definition. In this paper, a hierarchical clustering algorithm is proposed based on asymmetric distance. With respect to asymmetric distance characteristics, a selective factor and corresponding calculation formula are proposed. The single linkage, full linkage and average linkage algorithms for the asymmetric hierarchical clustering algorithm are implemented. The hot tags from main social bookmarking systems are extracted and an asymmetric distance is defined based on co-occurrence frequency of different tags. The experimental results show that the proposed algorithm outperforms the clustering algorithm based on symmetrical distance. The cophenetic coefficient is also used to evaluate effectiveness of the algorithm.
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2014, Vol. 27 
