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

• Main Authors: Sarnovsky, M., Butka, P., Pocsova, J.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithm design and analysis; Analytical models; Cloud computing; Clustering algorithms; Computational modeling; Data analysis; Lattices
• ISSN: 1543-9259; 2767-9462
• DOI: 10.1109/INES.2012.6249847

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.