Research Interests

Jan 2005 - Current

 I am currently working in one of the leading multinational IT services and consulting companies as an IT consultant in Information Management and Enterprise Content Management. In my spare time I have been continuing my own research related to my PhD topic. I have and will be publishing and presenting articles at major international conferences. For this I have been employing best software practices to develop a web application that can query a web search engine and crawl the resulting pages. The documents are then organised using an improved online version of the adaptive topological tree method.

(more to follow)

Jan 2004 - PhD write-up, enhancements to Topological Trees and further experiments/results.

Now entering the most difficult part of the Ph.D., the final write-up stage! I have lots of ideas but no enough time to realise them all.  I will publish these in future papers once my thesis is complete, as I will first write-up what I have done up to now. Some of the enhancements will be used for efficient information access, knowledge discovery and decision support. 

I have also proposed the topological tree a new kind of growing tree representation with features from graph analysis and generated through my proposed method. These adaptive topological trees use the hidden features present in all tree representations. It has novel properties making it well suited for exploring large disorganised datasets, discovering complex hidden relations and searching large information spaces of document sets. 

The thesis details how our work on the Adaptive Topological Tree Structure relates to the areas of enterprise information portals and business intelligence. The methods generate a new type of tree view, which resembles a graph structure yet can be efficiently and naturally represented as a tree and using XML.

Description of project Jan 2003 - Advanced prototypes in Java and journal publication of Topological Tree.

The entire application was ported to Java for flexibility and reuse. The implementation has gone further than the Visual C++ version and now includes other SOM implementations such as the basic SOM, Growing Grid and Growing Hierarchical SOM to allow a comparison between methods. The taxonomy generated by the proposed method has proven to be robust, scalable and extremely useful for browsing & performing knowledge discovery on the unexplored collection of documents. I also started to take a more focused look at using my algorithm in enterprise content management and knowledge management systems. 

Description of project Jan 2002 - Initial prototypes in C++ and conference publications.

I am now focusing on document organisation, analysis and retrieval using Self-Organising Maps, which may also be written as Self-Organizing Maps or simply SOM. The SOM is used to perform document clustering where, textual documents are automatically sorted into a dynamically built hierarchy of topics, solely using the documents contents. I am currently working on large sets of web documents, created by book publishers, and which describe their books. A fully functional  Windows OS application was implemented in Visual C++ MFC, to automatically cluster these documents into a hierarchy of topics or taxonomy. The method we call Treeview SOM (TV-SOM) allows documents to be clustered very rapidly, using a minimum number of nodes, into a treeview hierarchy of topics and without the use of any further post-processing (unlike other existing SOM methods). This type of view is optimised for user navigation and understanding.

This work is closely related to knowledge management, information retrieval and text mining. To build an effective system I believe that a true multi-disciplinary investigation has to be undertaken in the three areas of:

• Linguistics: such as semantics, discourse analysis, natural language understanding.
• Statistics: clustering, compression and co-occurrence analysis.
• Computing techniques: neural networks, Indexing and Information Retrieval.

Description of project Jan 2001 - In depth review of information retrieval and document clustering. Detailed planning and layout of ideas.

The original MPhil title was:

"Neural Networks for multimedia data exploration and visualization"

Technology for improving computing power has focused much on increasing processing speed. Whilst increasing computing efficiency has also become a demanding issue in today and future computing world as resources and information volume grow as astonishing speeds both locally and across the internet. Efficient data management and intelligent information retrieval methods will stand as important functions in future computing and information systems. The project will address and seek the novel methods in exploring and demonstrating multidimensional, multisensory and multimedia data by means of neural networks, self-organizing systems in particular. As the human cognition system, which often has multisensory inputs, has various levels of pattern association and effective information retrieval. It also is error resilient. The aims and objectives of the project are to achieve a better understanding of the human cognition system in relation to pattern association, to model the process using neural networks, fuzzy logic and genetic algorithms, and to produce corresponding novel computer systems for managing web or multimedia data/ documents. The project will also involve studies of statistical methods such as multidimensional scaling and factor analysis and development of computer software. 

List of Selected Publications

Richard Freeman, "Topological Tree Clustering of Social Network Search Results" in Proceedings of the Eight International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'07), Lecture Notes in Computer Science (LNCS 4481), Springer, 16-19 December, 2007, pp.  760-769 [Abstract] [Article]

Richard T. Freeman, "Topological Tree Clustering of Web Search Results" in Proceedings of the Seventh International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'06), Lecture Notes in Computer Science (LNCS 4224), Springer, 20-23 September, 2006, pp.  789-797 [Abstract] [Article]

R Freeman and H Yin, "Web Content Management by Self-Organisation," IEEE Transactions on Neural Networks, Special Issue on Adaptive Learning Systems in Communication Networks, Vol. 16(5), pp. 1256-1268, 2005 [Abstract] [Article].

Richard Freeman and H Yin, "Tree view self-organisation of web content," Neurocomputing, Vol. 63, pp. 415-446, 2005 [Abstract] [Article].

Richard T. Freeman, "Web Document Search, Organisation and Exploration Using
Self-Organising Neural Networks", PhD Thesis, School of Electrical & Electronic Engineering, Faculty of Engineering and Physical Sciences, University of Manchester, 2004 [Abstract].

Richard Freeman and Hujun Yin, "Topological Tree for Web Organisation, Discovery and Exploration" in Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'04), Lecture Notes in Computer Science (LNCS 3177), Springer, 25-27 August, 2004, pp. 478-484. [Abstract] [Article]

Richard T. Freeman and Hujun Yin, "Adaptive Topological Tree Structure for Document Organisation and Visualisation", Neural Networks, 17(8-9):1255-1271, 2004 [Abstract] [Article].

H Yin, N M Allinson, R Freeman, J Keane, and S Hubbard (Eds.), Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science (LNCS 2412), Springer: Berlin, 2002.

Richard Freeman and Hujun Yin, "Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information", in Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'02), Lecture Notes in Computer Science (LNCS 2412), Springer-Verlag, 12-14 August, 2002, pp. 123-128. [Abstract] [Article] [PDF]

Richard Freeman, Hujun Yin and Nigel M. Allinson, "Self-Organising Maps for Tree View Based Hierarchical Document Clustering", in Proceedings of the International Joint Conference on Neural Networks (IJCNN'02) part of the IEEE 2002 World Congress on Computational Intelligence (WCCI 2002), Honolulu, Hawaii, 12-17 May, 2002. vol. 2, pp. 1906-1911. [Abstract] [Article] [PDF]

Richard Freeman and Hujun Yin, "Automated Document Organisation Using Self-Organising Maps", presented at EPSRC PREP 2002, Nottingham University, UK, 17-19 April 2002.

Richard Freeman, "Self-Organising Maps for automated hierarchical clustering and labelling of Web documents", MPhil dissertation, University of Manchester, 2001