Name
Abstract | ||
|---|---|---|
In this paper, we define the problem of topic-driven clustering, which organizes a document collection according to a given set of topics. We propose three topic-driven schemes that consider the similarity between documents and topics and the relationship among documents themselves simultaneously. We present a comprehensive experimental evaluation of the proposed topic-driven schemes on five datasets. Our experimental results show that the proposed topic-driven schemes are efficient and effective with topic prototypes of different levels of specificity. |
Year | Venue | Field |
|---|---|---|
2005 | SIAM Proceedings Series | Clustering high-dimensional data,Information retrieval,Document clustering,Computer science,Artificial intelligence,Cluster analysis,Machine learning |
DocType | Citations | PageRank |
Conference | 13 | 0.70 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhao | 1 | 902 | 49.19 |
| George Karypis | 2 | 15691 | 1171.82 |