Incremental clustering for dynamic information processing
ACM Transactions on Information Systems (TOIS)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
A vector space model for automatic indexing
Communications of the ACM
Incremental Document Clustering Using Cluster Similarity Histograms
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
Information Retrieval: Algorithms and Heuristics (The Kluwer International Series on Information Retrieval)
Incremental hierarchical clustering of text documents
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
On the utility of incremental feature selection for the classification of textual data streams
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Incremental clustering of newsgroup articles
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Aggregated search: A new information retrieval paradigm
ACM Computing Surveys (CSUR)
| Hi-index | 0.00 |
There is an increasing need for online news aggregation and visualization. Commercial systems, such as Google News and Ask.com, have successfully launched a portal aiming at providing an aggregated view of the top news events at a given time. However, these systems, as well as previous research projects, lack the ability to personalize events according to the user's need. Furthermore, users increasingly prefer to see multiple types of media to be presented when they follow a particular event of interest. In this paper, we describe a novel framework to allow the aggregation of online sources for text articles, images, videos and TV news into news stories, while the visualization enables the users to browse and select the news events based on semantic information. The experimental results have indicated some promising results