BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Information Retrieval
Self-Organizing Maps
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Combining multiple clustering systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Immune Learning in a Dynamic Information Environment
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Towards adaptive web mining: histograms and contexts in text data clustering
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Contextual adaptive clustering of web and text documents with personalization
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
A review of evolutionary and immune-inspired information filtering
Natural Computing: an international journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Text data clustering by contextual graphs
DS'06 Proceedings of the 9th international conference on Discovery Science
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We present a novel approach to incremental document maps creation, which relies upon partition of a given collection of documents into a hierarchy of homogeneous groups of documents represented by different sets of terms. Further each group (defining in fact separate context) is explored by a modified version of the aiNet immune algorithm to extract its inner structure. The immune cells produced by the algorithm become reference vectors used in preparation of the final document map. Such an approach proves to be robust in terms of time and space requirements as well as the quality of the resulting clustering model.