Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Self-Organizing Maps
Enabling Context-Aware Agents to Understand Semantic Resources on The WWWand The Semantic Web
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Context-orientated news riltering for web 2.0 and beyond
Proceedings of the 15th international conference on World Wide Web
Mining blog stories using community-based and temporal clustering
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Identifying and characterizing public science-related fears from RSS feeds: Research Articles
Journal of the American Society for Information Science and Technology
A novel clustering-based RSS aggregator
Proceedings of the 16th international conference on World Wide Web
Clustering Blogs with Collective Wisdom
ICWE '08 Proceedings of the 2008 Eighth International Conference on Web Engineering
Topological tree clustering of social network search results
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
PNS: personalized multi-source news delivery
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Topological tree clustering of web search results
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Web content management by self-organization
IEEE Transactions on Neural Networks
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With the rapid and dramatic increase in web feeds published by different publishers, providers or websites via Really Simple Syndication (RSS) and Atom, users cannot be expected to scan, select and consume all the content manually. This is leading to an information overload for consumers as the amount of content increases. With this growth there is a need to make the content more accessible and allow it to be efficiently searched and explored. This can be partially achieved by structuring and organising the content dynamically into topics or categories. Typical approaches make use of categorisation or clustering, however these approaches have a number of limitations such as the inability to represent the connections between topics and being heavy dependent on fixed parameters. In this paper we apply the topological tree method, to dynamically identify categories, on financial and business news feed dataset. The topological tree method is used to automatically organise an aggregation of the financial news feeds into self-discovered topics and allows a drill down into sub-topics. The news feeds, organised using the topological tree method, are discussed against those of typical web aggregators. A discussion is made on the criterions of representing news feeds, and the advantages of presenting underlying topics and providing a clear view of the connections between news topics. The topological tree has been found to be a superior representation, and well suited for organising financial news content and could be applied to categorise and filter news more efficiently for market abuse detection.