Name
Abstract | ||
|---|---|---|
In this paper, we propose the topic-constrained hierarchical clustering, which organizes document datasets into hierarchical trees consistant with a given set of topics. The proposed algorithm is based on a constrained agglomerative clustering framework and a semi-supervised criterion function that emphasizes the relationship between documents and topics and the relationship among documents themselves simultaneously. The experimental evaluation show that our algorithm outperformed the traditional agglomerative algorithm by 7.8% to 11.4%. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17316-5_17 | ADMA (1) |
Keywords | Field | DocType |
topic-constrained hierarchical clustering,semi-supervised criterion function,hierarchical trees consistant,agglomerative clustering framework,traditional agglomerative algorithm,document datasets,proposed algorithm,experimental evaluation show,hierarchical clustering,semi supervised learning | Hierarchical clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Correlation clustering,Computer science,Hierarchical clustering of networks,Artificial intelligence,Cluster analysis,Brown clustering,Machine learning,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
6440 | 0302-9743 | 3-642-17315-2 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
1 | ||