Martin Polovincak

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Our paper introduces well-known methods for compressing formal context and focuses on concept lattices and attribute implication base changes of compressed formal contexts. In this paper Singular Value Decomposition and Non-negative Matrix Factorisation methods for compressing formal context are discussed. Computing concept lattices from reduced formal(More)
There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value(More)
High complexity of formal concept, analysis algorithms and lattice construction algorithms are main problems today. If we want to compute all concepts from huge incidence matrix, complexity plays a great role. In some cases, we do not need to compute all concepts, but only some of them. Our research focuses on behavior of the concept lattice reduction after(More)
There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Nonnegative Matrix Factorization (NMF) can(More)
One of the main problems connected with the formal concept analysis and lattice construction is the high complexity of algorithms which plays a significant role when computing all concepts from a huge incidence matrix. In some cases, we only need to compute some of them to test for common attributes. In our research we try to modify an incidence matrix(More)
High complexity of lattice construction algorithms and uneasy way of visualising lattices are two important problems connected with the formal concept analysis. Algorithm complexity plays significant role when computing all concepts from a huge incidence matrix. In this paper we try to modify an incidence matrix using matrix decomposition, creating a new(More)
The large volume of data from the large-scale computing platforms for high-fidelity design and simulations, and instrumentation for gathering scientific as well as business data, and huge information in the web, give us some problems if we want to compute all concepts from huge incidence matrix. In some cases, we do not need to compute all concepts, but(More)
There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Nonnegative Matrix Factorization (NMF) can(More)
Matrix reduction and formal concept analysis are two complementary mathematical tools for data analysis. In this paper, we study the reduction of the concept lattices and implication bases based on matrix reduction and propose two kinds of reduction methods for the above concept lattices. We analyse the benefits which we obtain by using several methods of(More)
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