Cloud computing as a platform for distributed fuzzy FCA approach in data analysis

In this paper we describe use of cloud computing platform for support of distributed creation of conceptual models based on the FCA (Formal Concept Analysis) framework. FCA is one of the approaches which can be applied in process of conceptual data analysis. Extension of classical FCA (binary table data) is (one-sided) fuzzy version that works with different types of lattice-based attributes (binary, ordinal, interval-based, etc.) in the object-attribute table. This extension, so-called generalized one-sided concept lattices, provide possibility for researcher or data analyzer to use fuzzy FCA for object-attribute tables without the need for specific unified pre-processing, what is usually expected in practical data mining or online analytical tools. Computational complexity of creation of concept lattices from large contexts (data tables) is considerable, also interpretability of huge concept lattices is problematic. Therefore, we will also propose a solution for creation of simple hierarchy of smaller FCA models. Starting data table is decomposed into smaller sets of objects and then one concept lattice is built for every subset using generalized one-sided concept lattice. Such small FCA-based models are better for interpretability, and also can be combined into one hierarchy of models using simple hierarchical clustering based on the descriptions of particular models (as weighted vectors of attributes), which can be searched in analytical tool by data analyst. Cloud infrastructure is then used for increase of computational effectiveness, because particular models are built in parallel/distributed way. This cloud module can be a part of more complex data analytical system, which is also presented at the end of the paper.

[1]  Peter Butka,et al.  Generalization of One-Sided Concept Lattices , 2013, Comput. Informatics.

[2]  A. Jaoua,et al.  Discovering knowledge from fuzzy concept lattice , 2001 .

[3]  Bernhard Ganter,et al.  Formal Concept Analysis , 2013 .

[4]  Peter Butka,et al.  Grid-based Support for Different Text Mining Tasks , 2009 .

[5]  Peter Butka,et al.  On generation of one-sided concept lattices from restricted context , 2012, 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics.

[6]  Jozef Pócs,et al.  Note on generating fuzzy concept lattices via Galois connections , 2012, Inf. Sci..

[7]  L. Beran,et al.  [Formal concept analysis]. , 1996, Casopis lekaru ceskych.

[8]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  Emil Popescu,et al.  On Galois Connexions , 1994 .

[11]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[12]  Walter Ruda,et al.  Target Group-Specific Design of Student Entrepreneurship Support - A German Example Focusing on Start-Up Motives and Barriers , 2009 .

[13]  P. Butka,et al.  One approach to combination of FCA-based local conceptual models for text analysis — grid-based approach , 2008, 2008 6th International Symposium on Applied Machine Intelligence and Informatics.