Compatible attribute subcontexts of one-sided vague formal concept lattices

2020 ◽  
Author(s):  
Nafiseh Hadidi ◽  
Shokoofeh Ghorbani
Keyword(s):  
Formal Concept ◽  
Concept Lattices

Browsing Large Concept Lattices through Tree Extraction and Reduction Methods

2013 ◽  
Vol 9 (4) ◽  
pp. 16-34 ◽  
Author(s):  
Cassio Melo ◽  
Bénédicte Le-Grand ◽  
Marie-Aude Aufaure
Keyword(s):  
Fault Tolerance ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Hasse Diagram ◽  
Formal Concept ◽  
Concept Lattices ◽  
Direct Graph ◽  
Concept Clustering ◽  
Reduction Methods ◽  
Tree Extraction

Browsing concept lattices from Formal Concept Analysis (FCA) becomes a problem as the number of concepts can grow significantly with the number of objects and attributes. Interpreting the lattice through direct graph-based visualisation of the Hasse diagram rapidly becomes difficult and more synthetic representations are needed. In this work the authors propose an approach to simplify concept lattices by extracting and visualising trees derived from them. The authors further simplify the browse-able trees with two reduction methods: fault-tolerance and concept clustering.

The construction of multi-granularity concept lattices

2020 ◽  
Vol 39 (3) ◽  
pp. 2783-2790
Author(s):  
Qian Hu ◽  
Ke-Yun Qin
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Mutual Transformation ◽  
Concept Lattices ◽  
Construction Methods ◽  
Formal Concepts ◽  
Transformation Methods

The construction of concept lattices is an important research topic in formal concept analysis. Inspired by multi-granularity rough sets, multi-granularity formal concept analysis has become a new hot research issue. This paper mainly studies the construction methods of concept lattices in multi-granularity formal context. The relationships between concept forming operators under different granularity are discussed. The mutual transformation methods of formal concepts under different granularity are presented. In addition, the approaches of obtaining coarse-granularity concept lattice by fine-granularity concept lattice and fine-granularity concept lattice by coarse-granularity concept lattice are examined. The related algorithms for generating concept lattices are proposed. The practicability of the method is illustrated by an example.

scholarly journals A New Rapid Incremental Algorithm for Constructing Concept Lattices

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 78 ◽  
Author(s):  
Jingpu Zhang ◽  
Ronghui Liu ◽  
Ligeng Zou ◽  
Licheng Zeng
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Incremental Algorithm ◽  
Formal Context ◽  
Formal Concept ◽  
Concept Lattices ◽  
Covering Relation ◽  
Canonical Generator ◽  
Lattice Construction ◽  
Time And Space Complexity

Formal concept analysis has proven to be a very effective method for data analysis and rule extraction, but how to build formal concept lattices is a difficult and hot topic. In this paper, an efficient and rapid incremental concept lattice construction algorithm is proposed. The algorithm, named FastAddExtent, is seen as a modification of AddIntent in which we improve two fundamental procedures, including fixing the covering relation and searching the canonical generator. The proposed algorithm can locate the desired concept quickly by adding data fields to every concept. The algorithm is depicted in detail, using a formal context to show how the new algorithm works and discussing time and space complexity issues. We also present an experimental evaluation of its performance and comparison with AddExtent. Experimental results show that the FastAddExtent algorithm can improve efficiency compared with the primitive AddExtent algorithm.

Ontology Construction Using Multiple Concept Lattices

2014 ◽  
Vol 926-930 ◽  
pp. 1975-1978
Author(s):  
Wen Chao Wang ◽  
Jiang Lu
Keyword(s):  
Formal Concept Analysis ◽  
Domain Ontology ◽  
Complex Domain ◽  
Formal Concept ◽  
Target Domain ◽  
Concept Lattices ◽  
Ontology Construction ◽  
Large Domain ◽  
Fuzzy Formal Concept ◽  
Fuzzy Formal Concept Analysis

The paper proposes an ontology construction approach that combines Fuzzy Formal Concept Analysis, Wikipedia and WordNet in a process that constructs multiple concept lattices for sub-domains. Those sub-domains are divided from the target domain. The multiple concept lattices approach can mine concepts and determine relations between concepts automatically, and construct domain ontology accordingly. This approach is suitable for the large domain or complex domain which contains obvious sub-domains.

CHARACTERIZING TREES IN CONCEPT LATTICES

2008 ◽  
Vol 16 (supp01) ◽  
pp. 1-15 ◽  
Author(s):  
RADIM BĚLOHLÁVEK ◽  
BERNARD DE BAETS ◽  
JAN OUTRATA ◽  
VILEM VYCHODIL
Keyword(s):  
Formal Concept Analysis ◽  
Input Data ◽  
Concept Lattice ◽  
Sufficient Conditions ◽  
Formal Concept ◽  
Clustering Methods ◽  
Concept Lattices ◽  
Classification Schemes ◽  
Formal Concepts ◽  
Necessary And Sufficient

Concept lattices are systems of conceptual clusters, called formal concepts, which are partially ordered by the subconcept/superconcept relationship. Concept lattices are basic structures used in formal concept analysis. In general, a concept lattice may contain overlapping clusters and need not be a tree. On the other hand, tree-like classification schemes are appealing and are produced by several clustering methods. In this paper, we present necessary and sufficient conditions on input data for the output concept lattice to form a tree after one removes its least element. We present these conditions for input data with yes/no attributes as well as for input data with fuzzy attributes. In addition, we show how Lindig's algorithm for computing concept lattices gets simplified when applied to input data for which the associated concept lattice is a tree after removing the least element. The paper also contains illustrative examples.

scholarly journals Attribute Reduction of Intuitionistic Fuzzy Concept Lattices

2015 ◽  
Vol 713-715 ◽  
pp. 1649-1654 ◽  
Author(s):  
Hong Wang ◽  
Hong Juan Zhang
Keyword(s):  
Information Systems ◽  
Attribute Reduction ◽  
Dominance Relation ◽  
Formal Concept ◽  
Fuzzy Information ◽  
Concept Lattices ◽  
Fuzzy Concept ◽  
Intuitionistic Fuzzy

In this paper, we turn intuitionistic fuzzy information systems into 0-1 formal contexts by using dominance relation. A pair of operators is defined to get the formal concept lattices in the intuitionistic fuzzy information systems. Furthermore, some properties and attribute reduction based on discernibility matrices is investigated.

Formal concept analysis via multi-adjoint concept lattices

2009 ◽  
Vol 160 (2) ◽  
pp. 130-144 ◽  
Author(s):  
Jesús Medina ◽  
Manuel Ojeda-Aciego ◽  
Jorge Ruiz-Calviño
Keyword(s):  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Concept Lattices

A Concept Lattice Merger Approach for Ontology Construction

2011 ◽  
Vol 181-182 ◽  
pp. 667-672
Author(s):  
Bao Chuan Han ◽  
Ya Jun Du ◽  
Chang Wang ◽  
Jing Xu
Keyword(s):  
Semantic Representation ◽  
Concept Lattice ◽  
Domain Ontology ◽  
Semantic Relations ◽  
Formal Concept ◽  
Concept Lattices ◽  
Ontology Construction ◽  
Semantic Query ◽  
Hierarchical Relations ◽  
Ontology Tree

The method of merging concept lattice in domain ontology construction can describe the implicit concepts and relationships between concepts more appropriately for semantic representation and query match. In order to enrich semantic query, the paper intends to apply the theory of Formal Concept Analysis (FCA) to establish source concept lattices, through which the domain concepts are extracted from source concept lattices to generate the optimized concept lattice. Then, the ontology tree is generated by lattice mapping ontology algorithm (LMOA) combing some hierarchical relations in the optimized concept lattice. The experiment proves that the domain ontology can be achieved effectively by merging concept lattices and provide the semantic relations more precisely.

Sign in / Sign up

Export Citation Format

Share Document