Abstract
In data mining, Apriori algorithm for association rules mining is a traditional approach. However, it takes too much time in scanning database for finding the frequent itemsets. In this paper, based on SOM clustering, a novel algorithm is introduced. In this algorithm, each transaction is converted to an input vector, SOM is employed to train these input vectors, from which we achieve the visualization of the relationship between the items in a database. The time efficiency and the visualized map units make the proposed approach a particularly attractive alternative to current data mining algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining – Concepts and Techniques. Higher Education Press, Beijing (2001)
Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-organizing Map: Exploratory Analysis of High-dimensional Data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)
Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., Saarela, A.: Self-organization of a Massive Document Collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)
Vesanto, J., Alhoniemi, E.: Clustering of the Self-organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Kohonen, T.: Self-organizing Maps. Springer, Heidelberg (1995)
Debock, G., Kohonen, T.: Visual Exploration in Finance Using Self-organizing Maps. Springer, London (1998)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In Proc. 1994 Int. Conf. Very Large Databases (VLDB 1994) (1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. of the Int’l Conference on Data Engineering (ICDE), Taipei, Taiwan (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, S., Zhang, Y. (2004). Self-Organizing Feature Map Based Data Mining. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive