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An Efficient Hybrid Hierarchical Document Clustering Method

Yehang Zhu, Benjamin C. M. Fung, Dejun Mu, Yanling Li

Open publisher page 6 citations

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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What this paper is about

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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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Hierarchical clustering, Brown clustering, Cluster analysis, Hierarchical clustering of networks, Computer science, Single-linkage clustering, Document clustering, Hierarchy

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