Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Software library construction from an IR perspective
ACM SIGIR Forum
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
An Information Retrieval Approach for Automatically Constructing Software Libraries
IEEE Transactions on Software Engineering
A graphical, self-organizing approach to classifying electronic meeting output
Journal of the American Society for Information Science
Self-organizing maps
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
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The Self-Organising Map (SOM) is widely used to classify document collections. Such classifications are usually coarse-grained and cannot accommodate accurate document retrieval. A document classification scheme based on Multi-level Nested Self-Organising Map (MNSOM) is proposed to solve the problem. An MNSOM consists of a top map and a set of nested maps organised at different levels. The clusters on the top map of an MNSOM are at a relatively general level achieving retrieval recall, and the nested maps further elaborate the clusters into more specific groups, thus enhancing retrieval precision. The MNSOM was tested by a software document collection. The experimental results reveal that the MNSOM significantly improved the retrieval performance in comparison with the single SOM based classification.