Triadic Concept Analysis of Data with Fuzzy Attributes

Triadic concept analysis departs from the dyadic case by taking into account modi, such as time instances or conditions, under which objects have attributes. That is, instead of a two-dimensional table filled with 0s and 1s (equivalently, binary relation or two-dimensional binary matrix) which represents the input data to (dyadic) formal concept analysis, the input data to triadic concept analysis consists of a three-dimensional table (equivalently, ternary relation or three-dimensional binary matrix). In the ordinary triadic concept analysis, one assumes that the ternary relationship between objects, attributes, and modi, which specifies whether a given object has a given attribute under a given modus, is a yes-or-no relationship. In the present paper, we show how triadic concept analysis may be developed in a setting in which the ternary relationship between objects, attributes, and modi is a matter of degree rather than a yes-or-no relationship. We generalize the main results of the ordinary triadic concept analysis and outline applications of the presented notions and results as well as directions for future research.

[1]  Rasmus Bro,et al.  Multi-way Analysis with Applications in the Chemical Sciences , 2004 .

[2]  Ronald Fagin,et al.  Combining fuzzy information: an overview , 2002, SGMD.

[3]  R. Belohlavek,et al.  Optimal decompositions of matrices with grades , 2008, 2008 4th International IEEE Conference Intelligent Systems.

[4]  Rudolf Wille,et al.  A Triadic Approach to Formal Concept Analysis , 1995, ICCS.

[5]  Radim Belohlávek,et al.  Similarity relations in concept lattices , 2000, J. Log. Comput..

[6]  J. A. Goguen,et al.  The logic of inexact concepts , 1969, Synthese.

[7]  Vilém Vychodil,et al.  Discovery of optimal factors in binary data via a novel method of matrix decomposition , 2010, J. Comput. Syst. Sci..

[8]  R. P. Dilworth,et al.  Residuated Lattices. , 1938, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Vilém Vychodil,et al.  Factor Analysis of Incidence Data via Novel Decomposition of Matrices , 2009, ICFCA.

[10]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[11]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[12]  Radim Belohlávek,et al.  Concept lattices and order in fuzzy logic , 2004, Ann. Pure Appl. Log..

[13]  P. Kroonenberg Applied Multiway Data Analysis , 2008 .

[14]  Rudolf Wille,et al.  The Basic Theorem of triadic concept analysis , 1995 .

[15]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[16]  David E. Booth,et al.  Multi-Way Analysis: Applications in the Chemical Sciences , 2005, Technometrics.

[17]  Andreas Hotho,et al.  TRIAS--An Algorithm for Mining Iceberg Tri-Lattices , 2006, Sixth International Conference on Data Mining (ICDM'06).

[18]  C.J.H. Mann,et al.  Fuzzy Relational Systems: Foundations and Principles , 2003 .

[19]  Radim Bělohlávek,et al.  Fuzzy Relational Systems: Foundations and Principles , 2002 .

[20]  L. Beran,et al.  [Formal concept analysis]. , 1996, Casopis lekaru ceskych.

[21]  Petr Hájek,et al.  Metamathematics of Fuzzy Logic , 1998, Trends in Logic.