Hidden functional dependencies found by the technique of F-transform

In this contribution we provide an application of the technique of F–transform. We demonstrate that by using a simple density-based preprocessing, the applicability of F–transform in data analysis can be significantly improved. Despite of the fact that our procedure is demonstrated by a well-known DBSCAN algorithm and the technique of F–transform, the ideas are general enough to be applied for other density-based clustering algorithms and regression techniques, respectively.

[1]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[2]  Vilém Novák,et al.  Analysis and prediction of time series using fuzzy transform , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[3]  Marek Vajgl,et al.  Advanced F-Transform-Based Image Fusion , 2012, Adv. Fuzzy Syst..

[4]  Kenneth E. Barner,et al.  Fuzzy transformation and its applications , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[5]  Irina Perfilieva,et al.  Fuzzy transforms: Theory and applications , 2006, Fuzzy Sets Syst..

[6]  V. Kreinovich,et al.  Probabilistic Interpretation of Fuzzy Transforms and Fuzzy Control , 2009 .

[7]  Martin Stepnicka,et al.  Numerical solution of partial differential equations with help of fuzzy transform , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[8]  Radek Valasek,et al.  Full fuzzy transform and the problem of image fusion , 2006 .

[9]  Martina Danková,et al.  F-Transform Based Image Fusion , 2011 .

[10]  Vilém Novák,et al.  Fuzzy transform in the analysis of data , 2008, Int. J. Approx. Reason..