Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing feature trajectories for event detection
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic online news issue construction in web environment
Proceedings of the 17th international conference on World Wide Web
Web video topic discovery and tracking via bipartite graph reinforcement model
Proceedings of the 17th international conference on World Wide Web
A unified framework for web video topic discovery and visualization
Pattern Recognition Letters
Query-Guided Event Detection From News and Blog Streams
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An effective multi-clue fusion approach for web video topic detection
Proceedings of the 20th ACM international conference on Multimedia
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Although lots of work has been done since NIST proposed the problem of Topic Detection and Tracking (TDT), most of them focus on single media data. Topic detection for cross-media data hasn't been fully investigated. In this paper, we propose an effective method for cross-media topic detection. Unlike traditional topic detection methods that are mainly based on clustering, we consider using hot search queries as guidance to detect topics. Besides, we propose an improved co-clustering method which can be well suited for cross-media data clustering. First, we use queries to detect topics directly, and find the data associated with the topic. Second, we apply our co-clustering method to find the topics existing in the rest of data. Finally, the results obtained by the first two steps are threaded together as topics. Experiments show that our method can effectively detect topics for cross-media data.