Micro and macro models of granular computing induced by the indiscernibility relation

In rough set theory (RST), and more generally in granular computing on information tables (GRC-IT), a central tool is the Pawlak's indiscernibility relation between objects of a universe set with respect to a fixed attribute subset. Let us observe that Pawlak's relation induces in a natural way an equivalence relation ź on the attribute power set that identifies two attribute subsets yielding the same indiscernibility partition. We call indistinguishability relation of a given information table I the equivalence relation ź, that can be considered as a kind of global indiscernibility. In this paper we investigate the mathematical foundations of indistinguishability relation through the introduction of two new structures that are, respectively, a complete lattice and an abstract simplicial complex. We show that these structures can be studied at both a micro granular and a macro granular level and that are naturally related to the core and the reducts of I . We first discuss the role of these structures in GrC-IT by providing some interpretations, then we prove several mathematical results concerning the fundamental properties of such structures.

[1]  John G. Stell,et al.  Formal Concept Analysis over Graphs and Hypergraphs , 2013, GKR.

[2]  Hiroshi Tanaka,et al.  AQ, Rough Sets, and Matroid Theory , 1993, RSKD.

[3]  John G. Stell,et al.  Granulation for Graphs , 1999, COSIT.

[4]  Yiyu Yao,et al.  Rough set approximations in formal concept analysis , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[5]  Kun She,et al.  A matroidal approach to rough set theory , 2013, Theor. Comput. Sci..

[6]  Leonidas S. Pitsoulis,et al.  Topics in Matroid Theory , 2013 .

[7]  Davide Ciucci,et al.  Rough Set Theory Applied to Simple Undirected Graphs , 2015, RSKT.

[8]  Andrzej Skowron,et al.  Information systems in modeling interactive computations on granules , 2010, Theor. Comput. Sci..

[9]  Andrzej Skowron,et al.  Rough sets and Boolean reasoning , 2007, Inf. Sci..

[10]  Yee Leung,et al.  Granular Computing and Knowledge Reduction in Formal Contexts , 2009, IEEE Transactions on Knowledge and Data Engineering.

[11]  William Zhu,et al.  On the matroidal structure of generalized rough set based on relation via definable sets , 2016, Int. J. Mach. Learn. Cybern..

[12]  Qing Liu,et al.  Granular computing approach to finding association rules in relational database , 2010 .

[13]  Silvia Martínez Sanahuja,et al.  New rough approximations for n-cycles and n-paths , 2016, Appl. Math. Comput..

[14]  Sanyang Liu,et al.  Matroidal approaches to rough sets via closure operators , 2012, Int. J. Approx. Reason..

[15]  William Zhu,et al.  The Matroidal Structures of the Second Type of Covering-Based Rough Set , 2015, RSKT.

[16]  Jouni Järvinen,et al.  Lattice Theory for Rough Sets , 2007, Trans. Rough Sets.

[17]  Yiyu Yao,et al.  Set-theoretic Approaches to Granular Computing , 2012, Fundam. Informaticae.

[18]  Huangjian Yi,et al.  Rough sets and matroids from a lattice-theoretic viewpoint , 2016, Inf. Sci..

[19]  Yiyu Yao,et al.  The two sides of the theory of rough sets , 2015, Knowl. Based Syst..

[20]  Witold Pedrycz,et al.  Granular representation and granular computing with fuzzy sets , 2012, Fuzzy Sets Syst..

[21]  Claude Berge,et al.  Hypergraphs - combinatorics of finite sets , 1989, North-Holland mathematical library.

[22]  Tsau Young Lin,et al.  Database Mining on Derived Attributes , 2002, Rough Sets and Current Trends in Computing.

[23]  Davide Ciucci,et al.  Rough Set Theory and Digraphs , 2017, Fundam. Informaticae.

[24]  Davide Ciucci,et al.  The granular partition lattice of an information table , 2016, Inf. Sci..

[25]  Gianpiero Cattaneo,et al.  An Investigation About Rough Set Theory: Some Foundational and Mathematical Aspects , 2011, Fundam. Informaticae.

[26]  Tsau Young Lin,et al.  Data Mining and Machine Oriented Modeling: A Granular Computing Approach , 2000, Applied Intelligence.

[27]  Y. Yao,et al.  Stratified rough sets and granular computing , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[28]  Hua Mao Characterization and reduction of concept lattices through matroid theory , 2014, Inf. Sci..

[29]  John G. Stell Relational Granularity for Hypergraphs , 2010, RSCTC.

[30]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[31]  Tsau Young Lin,et al.  Granular Computing and Rough Sets - An Incremental Development , 2010, Data Mining and Knowledge Discovery Handbook.

[32]  Witold Pedrycz,et al.  Positive approximation: An accelerator for attribute reduction in rough set theory , 2010, Artif. Intell..

[33]  Hiroshi Tanaka,et al.  Algebraic Specification of Empirical Inductive Learning Methods based on Rough Sets and Matroid Theory , 1994, AISMC.

[34]  Tsau Young Lin,et al.  Granular Computing and Rough Sets , 2005, The Data Mining and Knowledge Discovery Handbook.

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

[36]  Davide Ciucci,et al.  Preclusivity and Simple Graphs: The n-cycle and n-path Cases , 2015, RSFDGrC.

[37]  Andrzej Skowron,et al.  Interactive information systems: Toward perception based computing , 2012, Theor. Comput. Sci..

[38]  Georg Gottlob,et al.  Identifying the Minimal Transversals of a Hypergraph and Related Problems , 1995, SIAM J. Comput..

[39]  Vladik Kreinovich,et al.  Handbook of Granular Computing , 2008 .

[40]  Gianpiero Cattaneo,et al.  On the connection of hypergraph theory with formal concept analysis and rough set theory , 2016, Inf. Sci..

[41]  Xiangping Kang,et al.  Formal concept analysis based on fuzzy granularity base for different granulations , 2012, Fuzzy Sets Syst..

[42]  Witold Pedrycz,et al.  Granular Computing - The Emerging Paradigm , 2007 .

[43]  Yiyu Yao,et al.  Granular Computing: basic issues and possible solutions , 2007 .

[44]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[45]  Davide Ciucci,et al.  Simple graphs in granular computing , 2016, Inf. Sci..

[46]  Yuhua Qian,et al.  Concept learning via granular computing: A cognitive viewpoint , 2014, Information Sciences.

[47]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

[48]  William Zhu,et al.  Rough matroids based on relations , 2013, Inf. Sci..

[49]  Yiyu Yao,et al.  A hypergraph model of granular computing , 2008, 2008 IEEE International Conference on Granular Computing.

[50]  Tsau Young Lin,et al.  Granular and rough computing on covering , 2012, 2012 IEEE International Conference on Granular Computing.

[51]  William Zhu,et al.  Matrix approaches to rough sets through vector matroids over fields , 2014, Int. J. Granul. Comput. Rough Sets Intell. Syst..

[52]  R. P. Dilworth Review: G. Birkhoff, Lattice theory , 1950 .

[53]  Yiyu Yao,et al.  On modeling data mining with granular computing , 2001, 25th Annual International Computer Software and Applications Conference. COMPSAC 2001.

[54]  Qingxin Zhu,et al.  Graph and matrix approaches to rough sets through matroids , 2014, Inf. Sci..

[55]  Piotr Honko,et al.  Relational pattern updating , 2012, Inf. Sci..

[56]  Yiyu Yao,et al.  A Granular Computing Approach to Machine Learning , 2002, FSKD.

[57]  Gianpiero Cattaneo,et al.  Abstract Approximation Spaces for Rough Theories , 2008 .

[58]  R. Stanley What Is Enumerative Combinatorics , 1986 .

[59]  Davide Ciucci,et al.  Generalizations of Rough Set Tools Inspired by Graph Theory , 2016, Fundam. Informaticae.

[60]  Giampiero Chiaselotti,et al.  Rough Sets for n-Cycles and n-Paths , 2016 .

[61]  Yiyu Yao,et al.  A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis , 2004, Rough Sets and Current Trends in Computing.

[62]  Yiyu Yao,et al.  Granular Computing as a Basis for Consistent Classification Problems , 2002 .

[63]  Y. Yao Information granulation and rough set approximation , 2001 .

[64]  Davide Ciucci,et al.  Simple Undirected Graphs as Formal Contexts , 2015, ICFCA.

[65]  E. F. Codd,et al.  A relational model of data for large shared data banks , 1970, CACM.

[66]  John G. Stell,et al.  Relations in Mathematical Morphology with Applications to Graphs and Rough Sets , 2007, COSIT.

[67]  Yiyu Yao,et al.  A Partition Model of Granular Computing , 2004, Trans. Rough Sets.

[68]  Gianpiero Cattaneo,et al.  Generalized Rough Sets (Preclusivity Fuzzy-Intuitionistic (BZ) Lattices) , 1997, Stud Logica.

[69]  Hui Li,et al.  Connectedness of Graph and Matroid by Covering-Based Rough Sets , 2015, RSFDGrC.

[70]  Shiping Wang,et al.  Rough Set Characterization for 2-circuit Matroid , 2014, Fundam. Informaticae.

[71]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[72]  Jinkun Chen,et al.  An application of rough sets to graph theory , 2012, Inf. Sci..

[73]  Tsau Young Lin,et al.  Data Mining: Granular Computing Approach , 1999, PAKDD.

[74]  Aiping Huang,et al.  Nullity-based matroid of rough sets and its application to attribute reduction , 2014, Inf. Sci..

[75]  R. Stanley Enumerative Combinatorics: Volume 1 , 2011 .

[76]  Piotr Honko,et al.  Association discovery from relational data via granular computing , 2013, Inf. Sci..

[77]  Piotr Honko,et al.  Description and classification of complex structured objects by applying similarity measures , 2008, Int. J. Approx. Reason..

[78]  Davide Ciucci,et al.  Preclusivity and Simple Graphs , 2015, RSFDGrC.

[79]  J. Stepaniuk Rough – Granular Computing in Knowledge Discovery and Data Mining , 2008 .

[80]  John G. Stell Relations on Hypergraphs , 2012, RAMICS.

[81]  Dmitry N. Kozlov,et al.  Combinatorial Algebraic Topology , 2007, Algorithms and computation in mathematics.