Krzysztof Najman
ARTICLE

(Polish) PDF

ABSTRACT

Since early 90s of 20th century has seen a steady and dynamic growth databases and collected information. There has also been a steady increase in demand for information, on the other hand growth in collection and storage information. One of the properties of some databases is their dynamic, changing during the group structure. The article presents an overview of the basic concepts of dynamic grouping and its proposed new definition. It was also a practical method to implement dynamic grouping based on self-learning neural network type of GNG. The results of simulation studies are presented in a dynamic grouping.

KEYWORDS

cluster analysis, dynamical clustering, GNG networks

REFERENCES

[1] Babcock B., Babu S., Datar M., Motwani R., Widom J. [2002], Models and issues in data stream systems. In Proceedings of the Twentyfirst ACMSIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 3-5, Madison, Wisconsin, USA, 1-16, ACM.

[2] Fenn D.J., Porter M.A., Mucha P.J., McDonald M., Williams S., Johnson N.F., Jones N.S. [2010], Dynamical Clustering of Exchange Rates, arXiv:0905.4912v2.

[3] Fritzke B. [1994], Growing cell structures - a self-organizing network for unsupervised and supervised learning, Neural Networks, 7, 9, 1441-1460.

[4] Guedalia I.D., London M., Werman M. [1999], An on-line agglomerative clustering method for non-stationary data, Neural Computation, 11, 2, 521-540.

[5] Guha S., Mishra N., Motwani R., O’Callaghan L. [2000], Clustering data streams. In IEEE Symposium on Foundations of Computer Science (FOCS), 359-366.

[6] Kohonen T. [1997], Self-Organizing Maps, Springer Series in Information Sciences, Springer-Verlag, Berlin Heidelberg.

[7] Migdał Najman K., Najman K., Data Analysis [2008], Machine Learning and Applications, Applying the Kohonen Self-organizing Map Networks to Selecting Variables, Studies in Classification, Data Analysis and Knowledge Organization, Presisach C., Burkhardt H., Schmidt-Thieme L., Decker R., Springer Verlag Berlin Heidelberg, 45-54.

[8] Najman K. [2009], Zastosowanie nienadzorowanych sieci neuronowych typu Growing Neural Gas w analizie skupień, Prace Naukowe UE we Wrocławiu, nr 47, 196-205.

[9] Najman K. [2010], Ocena wpływu parametrów sterujących procesem samouczenia się sieci GNG na ich zdolność do separowania skupień, Klasyfikacja i analiza danych – teoria i zastosowania Taksonomia 17, Prace Naukowe UE we Wrocławiu nr 17, 296-2010.

[10] Qin A. K. i Suganthan P. N. [2004], Robust growing neural gas algorithm with application in cluster analysis, Neural Networks, 17, 8-9, 1135-1148.

[11] Wang X., Smith K. Hyndman R. [1997], Characteristic-based clustering for time series data, Data mining and knowledge discovery, 13, 3, 335-364.

[12] Zamir O., Etzioni O. [1999], Grouper: A dynamic clustering interface to web search results, Proceeding of WWW8, Toronto, Canada.

Back to top
Copyright © 2019 Statistics Poland