Abstract
In this paper, we define the problem of topic-driven clustering, which organizes a document collection ac cording to a given set of topics. We propose three topic-driven schemes that consider the similarity be tween 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.
Original language | English (US) |
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Pages | 358-369 |
Number of pages | 12 |
DOIs | |
State | Published - 2005 |
Event | 5th SIAM International Conference on Data Mining, SDM 2005 - Newport Beach, CA, United States Duration: Apr 21 2005 → Apr 23 2005 |
Other
Other | 5th SIAM International Conference on Data Mining, SDM 2005 |
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Country/Territory | United States |
City | Newport Beach, CA |
Period | 4/21/05 → 4/23/05 |