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Improvement of the Multiple-View Learning Based on the Self-Organizing Maps

  • Tomasz Galkowski
  • Artur Starczewski
  • Xiuju Fu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9120)

Abstract

Big data sets and variety of data types lead to new types of problems in modern intelligent data analysis. This requires the development of new techniques and models. One of the important subjects is to reveal and indicate heterogeneous of non-trivial features of a large database. Original techniques of modelling, data mining, pattern recognition, machine learning in such fields like commercial behaviour of Internet users, social networks analysis, management and investigation of various databases in static or dynamic states have been recently investigated. Many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed. In this paper we have proposed a model for multiple view unsupervised clustering based on Kohonen self-organizing-map method.

Keywords

Fuzzy Cluster Hyperspectral Image Spectral Cluster Multiple View Concept Drift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland
  2. 2.Institute of High Performance ComputingSingaporeSingapore

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