BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Combining multiple clustering systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
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The increasing number of documents returned by search engines for typical requests makes it necessary to look for new methods of representation of contents of the results, like document maps. Though visually impressive, doc maps (e.g. WebSOM) are extensively resource consuming and hard to use for huge collections. In this paper, we present a novel approach, which does not require creation of a complex, global map-based model for the whole document collection. Instead, a hierarchy of topic-sensitive maps is created. We argue that such approach is not only much less complex in terms of processing time and memory requirement, but also leads to a robust map-based browsing of the document collection.