An Efficient Hybrid Hierarchical Document Clustering Method
Yehang Zhu, Benjamin C. M. Fung, Dejun Mu, Yanling Li
Abstract
Yehang Zhu, Benjamin C. M. Fung, Dejun Mu, Yanling Li
Abstract
Document clustering is a technique for grouping document objects together such that documents within a cluster have high similarity while documents in different clusters have low similarity. Hierarchical document clustering organizes the clusters into a hierarchy such that a parent cluster is a general topic of its child clusters. In this paper, we propose a novel hierarchical document clustering method that is a hybrid version of partitioning and agglomerative clustering approaches. The proposed method inherits the merit of efficiency from the partitioning approach and the hierarchical structure from agglomerative approach. Experiments on real-life datasets suggest that our method is effective and efficient.
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Document clustering is a technique for grouping document objects together such that documents within a cluster have high similarity while documents in different clusters have low similarity. Hierarchical document clustering organizes the clusters into a hierarchy such that a parent cluster is a general topic of its child clusters. In this paper, we propose a novel hierarchical document clustering method that is a hybrid version of partitioning and agglomerative clustering approaches. The proposed method inherits the merit of efficiency from the partitioning approach and the hierarchical structure from agglomerative approach. Experiments on real-life datasets suggest that our method is effective and efficient.
Key concepts: Hierarchical clustering, Brown clustering, Cluster analysis, Hierarchical clustering of networks, Computer science, Single-linkage clustering, Document clustering, Hierarchy