A practical approach to topic detection based on credible association rule mining

Topic detection is to develop automatic methods to identify topically related documents within a stream of data; many approaches have been developed to classify documents with predefined knowledge. This paper presents a new approach for topic detection and tracking based on credible association rule (CAR). This paper considers topic detection without any prior knowledge of category structure or possible categories. Topic features are selected primarily based on CAR. Results on the test set show a marginal improvement by using CAR and its maximal cliques mining algorithm. The CAR maximal cliques mining algorithm is now applied on real topic detection and tracking system which gives us a lot of experience in adjusting and refining the algorithm. This algorithm also presents many useful interface extensions for other modules of the system to use.