Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A graph-theoretic approach to extract storylines from search results
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Partitioning of Web graphs by community topology
WWW '05 Proceedings of the 14th international conference on World Wide Web
Graph-based text classification: learn from your neighbors
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting research topics via the correlation between graphs and texts
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Topic Detection Method Based on Bicharacteristic Vectors
NSWCTC '09 Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 02
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At present, most of the topic detection approaches are not accurate and efficient enough. In this paper, we proposed a new topic detection method (TPIC) based on an incremental clustering algorithm. It employs a self-refinement process of discriminative feature identification and a term reweighting algorithm to accurately cluster the given documents which discuss the same topic. To be efficient, the "aging" nature of topics is used to precluster stories. To automatically detect the true number of topics, Bayesian Information Criterion (BIC) is used to estimate the true number of topics. Experimental results on Linguistic Data Consortium (LDC) datasets TDT4 show that the proposed method can improve both the efficiency and accuracy, compared to other methods.