Formal concept analysis with negative attributes for forgery detection

Formal concept analysis with hierarchically ordered attributes

Formal concept analysis is a method of exploratory data analysis that aims at the extraction of natural clusters from object–attribute data tables. The clusters, called formal concepts, are naturally interpreted as human-perceived concepts in a traditional sense and can be partially ordered by a subconcept–superconcept hierarchy. The hierarchical structure of formal concepts (so-called concept ...

Granularity of attributes in formal concept analysis

Exploring Attributes with Domain Knowledge in Formal Concept Analysis

Recent literature reports the growing interests in data analysis using Formal Concept Analysis (FCA), in which data is represented in the form of object and attribute relations. FCA analyzes and then subsequently visualizes the data based on duality called Galois connection. Attribute exploration is a knowledge acquisition process in FCA, which interactively determines the implications holding ...

Selecting Attributes for Sport Forecasting using Formal Concept Analysis

In order to address complex systems, apply pattern recongnition on their evolution could play an key role to understand their dynamics. Global patterns are required to detect emergent concepts and trends, some of them with qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with i...

A Generalized Framework to Consider Positive and Negative Attributes in Formal Concept Analysis

In Formal Concept Analysis the classical formal context is analized taking into account only the positive information, i.e. the presence of a property in an object. Nevertheless, the non presence of a property in an object also provides a significant knowledge which can only be partially considered with the classical approach. In this work we have modified the derivation operators to allow the ...