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

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 technique...

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Bibliographic Details
Published inArtificial Intelligence and Soft Computing pp. 3 - 12
Main Authors Galkowski, Tomasz, Starczewski, Artur, Fu, Xiuju
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2015
SeriesLecture Notes in Computer Science
Subjects
Concept Drift
Fuzzy Cluster
Hyperspectral Image
Multiple View
Spectral Cluster
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Summary: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.
ISBN:3319193686
9783319193687
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-19369-4_1