Paper Info
Title
Scaling, Granulation, and Fuzzy Attributes in Formal Concept Analysis
Abstract
The present paper deals with scaling within the framework of formal concept analysis (FCA) of data with fuzzy attributes. In ordinary FCA, the input is a data table with yes/no attributes. Scaling is a process of transformation of data tables with general attributes, e.g. nominal, ordinal, etc., to data tables with yes/no attributes. This way, data tables with general attributes can be analyzed by means of FCA. We propose a new way of scaling, namely, scaling of general attributes to fuzzy attributes. After such a scaling, the data can be analyzed by means of FCA developed for data with fuzzy attributes. Compared to ordinary scaling to yes/no attributes, our scaling procedure is less sensitive to how a user defines a scale which eliminates the arbitrariness of user's definition of a scale. This is the main advantage of our approach. In addition, scaling to fuzzy attributes is appealing from the point of view of knowledge representation and is connected to Zadeh's concept of linguistic variable. We present a general definition of scaling, examples comparing our approach to ordinary scaling, and theorems which answer some naturally arising questions regarding sensitivity of FCA to the definition of a scale.
Year
DOI
Venue
2007
10.1109/FUZZY.2007.4295488
London
Keywords
Field
DocType
computational linguistics,data analysis,fuzzy set theory,knowledge representation,Zadeh linguistic variable concept,data table transformation,formal concept analysis,fuzzy attribute,knowledge representation,scaling process
Data mining,Knowledge representation and reasoning,Ordinal number,Computer science,Computational linguistics,Fuzzy logic,Fuzzy set,Scale (social sciences),Artificial intelligence,Scaling,Formal concept analysis,Machine learning
Conference
ISSN
ISBN
Citations 
1098-7584 E-ISBN : 1-4244-1210-2
1-4244-1210-2
2
PageRank 
References 
Authors
0.44
0
2
Name
Order
Citations
PageRank
Radim Belohlávek168781.38
Jan Konecny211517.20