Document Clustering Using the 1 + 1 Dimensional Self-Organising Map

Authors:
Ben Russell;Hujun Yin;Nigel M. Allinson
Affiliations:
-;-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

Citing 8
Cited 2

An evaluation of retrieval effectiveness for a full-text document-retrieval system

Communications of the ACM
The vocabulary problem in human-system communication

Communications of the ACM
Exploration of text collections with hierarchical feature maps

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Internet browsing and searching: user evaluations of category map and concept space techniques

Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
A vector space model for automatic indexing

Communications of the ACM
Information Retrieval

Information Retrieval
Automatically Detecting and Organizing Documents into Topic Hierarchies: A Neural Network Based Approach to Bookshelf Creation and Arrangement

ECDL '00 Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries
Self organization of a massive document collection

IEEE Transactions on Neural Networks

An optimized k-means algorithm of reducing cluster intra-dissimilarity for document clustering

WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Reorganizing clouds: A study on tag clustering and evaluation

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic clustering of documents is a task that has become increasingly important with the explosion of online information. The Self-Organising Map (SOM) has been used to cluster documents effectively, but efforts to date have used a single or a series of 2-dimensional maps. Ideally, the output of a document-clustering algorithm should be easy for a user to interpret. This paper describes a method of clustering documents using a series of 1- dimensional SOM arranged hierarchically to provide an intuitive tree structure representing document clusters. Wordnet is used to find the base forms of words and only cluster on words that can be nouns.