“…Other domains discuss the mathematics behind FCA with a fuzzy setting, an interval-valued fuzzy setting, possibility theory, a rough setting, a triadic, factor and incomplete context to apply these extensions in the appropriate context for knowledge processing tasks. Ferjani et al, 2012Feature extractions Formica, 2012 Semantic web Galitsky et al, 2013 Finding patterns on parse thickets Hamrouni et al, 2013 Finding some frequent itemset Li and Guo, 2013 Investigating formal query De Maio et al, 2014 Text mining Muangprathub et al, 2013 Classification Li andTsai, 2013 Text mining Finding cousins Neznanov and Kuznetsov, 2013 FCART tool Poshyvanyk et al, 2012 Concept location Priss, 2006 Application in information sciences Senatore and Pasi, 2013 Finding correlations Li and Tsai, 2013 Opinion classification Zerarga and Djouadi, 2013 Information retrieval Military intelligence Du and Hai, 2013 Mining web page Elzinga et al, 2010 Terrorist threat assessment Poelmans et al, 2013c Criminal trajectories Priss, 2011 Unix system monitoring Romanov et al, 2012 Detect anomalies Web services…”
Section: Ontology Engineering Research Goalmentioning
In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.
“…Other domains discuss the mathematics behind FCA with a fuzzy setting, an interval-valued fuzzy setting, possibility theory, a rough setting, a triadic, factor and incomplete context to apply these extensions in the appropriate context for knowledge processing tasks. Ferjani et al, 2012Feature extractions Formica, 2012 Semantic web Galitsky et al, 2013 Finding patterns on parse thickets Hamrouni et al, 2013 Finding some frequent itemset Li and Guo, 2013 Investigating formal query De Maio et al, 2014 Text mining Muangprathub et al, 2013 Classification Li andTsai, 2013 Text mining Finding cousins Neznanov and Kuznetsov, 2013 FCART tool Poshyvanyk et al, 2012 Concept location Priss, 2006 Application in information sciences Senatore and Pasi, 2013 Finding correlations Li and Tsai, 2013 Opinion classification Zerarga and Djouadi, 2013 Information retrieval Military intelligence Du and Hai, 2013 Mining web page Elzinga et al, 2010 Terrorist threat assessment Poelmans et al, 2013c Criminal trajectories Priss, 2011 Unix system monitoring Romanov et al, 2012 Detect anomalies Web services…”
Section: Ontology Engineering Research Goalmentioning
In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.
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