A methodology for analysis of concept lattice reduction

Independent methodology for analysis of concept lattice reduction.We use sets of proper implications holding in the original and reduced structure.Highlight the kinds of changes propitiated by the different classes of techniques.Four reduction techniques were used to illustrate the proposed methodology. Formal concept analysis (FCA) is a mathematical theory of data analysis with applications in many areas. The problem of obtaining a concept lattice of an appropriate size was identified in several applications as one of the most important problems of FCA. In order to deal with this problem several techniques with different characteristics were proposed for concept lattice reduction. However, there are currently no adequate methods to assess what types of knowledge transformations can result from a reduction. A methodology for analysis of concept lattice reduction is presented here. It is based on the use of sets of proper implications holding in the original and reduced formal contexts or concept lattices. Working with both sets of implications, the methodology is able to show what is preserved, eliminated, inserted or transformed by a reduction technique. Three classes of reduction techniques are analyzed from the standpoint of the methodology in order to highlight techniques of each class have in common with respect to the transformations performed. Such analysis is followed by specific examples in each class.

[1]  Wen-Xiu Zhang,et al.  Attribute reduction theory of concept lattice based on decision formal contexts , 2008, Science in China Series F: Information Sciences.

[2]  Bernard De Baets,et al.  Zoom-In/Zoom-Out Algorithms for FCA with Attribute Granularity , 2011, ISCIS.

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

[4]  Jonas Poelmans,et al.  Formal Concept Analysis in knowledge processing: A survey on models and techniques , 2013, Expert Syst. Appl..

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

[6]  Ming-Wen Shao,et al.  Attribute reduction in generalized one-sided formal contexts , 2017, Inf. Sci..

[7]  Sang-Eon Han,et al.  Lattice-theoretic contexts and their concept lattices via Galois ideals , 2016, Inf. Sci..

[8]  Cherukuri Aswani Kumar,et al.  FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING , 2012, Appl. Artif. Intell..

[9]  Jinhai Li,et al.  Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction , 2013, Int. J. Approx. Reason..

[10]  Vilém Vychodil,et al.  Closure-based constraints in formal concept analysis , 2013, Discret. Appl. Math..

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

[12]  Sérgio M. Dias,et al.  Knowledge reduction in formal contexts using non-negative matrix factorization , 2015, Math. Comput. Simul..

[13]  Xia Wang,et al.  Relations of attribute reduction between object and property oriented concept lattices , 2008, Knowl. Based Syst..

[14]  Karell Bertet,et al.  The multiple facets of the canonical direct unit implicational basis , 2010, Theor. Comput. Sci..

[15]  Sergei O. Kuznetsov,et al.  Approximating Concept Stability , 2012, ICFCA.

[16]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[17]  Mark A. J. Song,et al.  Using implications from FCA to represent a two mode network data , 2015, ICSE 2015.

[18]  Guoyin Wang,et al.  Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts , 2016, Knowl. Based Syst..

[19]  Bénédicte Le Grand,et al.  Conceptual and Spatial Footprints for Complex Systems Analysis: Application to the Semantic Web , 2009, DEXA.

[20]  Václav Snásel,et al.  On Concept Lattices and Implication Bases from Reduced Contexts , 2008, ICCS Supplement.

[21]  Sergei O. Kuznetsov,et al.  On stability of a formal concept , 2007, Annals of Mathematics and Artificial Intelligence.

[22]  Jinkun Chen,et al.  Relations of reduction between covering generalized rough sets and concept lattices , 2015, Inf. Sci..

[23]  Karell Bertet,et al.  The dependence Graph of a Lattice , 2012, CLA.

[24]  Aleksey Buzmakov,et al.  Scalable Estimates of Concept Stability , 2014, ICFCA.

[25]  P. Grünwald The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[26]  Camille Roth,et al.  Towards Concise Representation for Taxonomies of Epistemic Communities , 2006, CLA.

[27]  P. Grünwald The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[28]  Xizhao Wang,et al.  Comparison of reduction in formal decision contexts , 2017, Int. J. Approx. Reason..

[29]  Hong Wang,et al.  Approaches to knowledge reduction in generalized consistent decision formal context , 2008, Math. Comput. Model..

[30]  Sergei O. Kuznetsov,et al.  Concept Interestingness Measures: a Comparative Study , 2015, CLA.

[31]  Zhang Wen-xiu,et al.  Attribute reduction theory and approach to concept lattice , 2005 .

[32]  Ming-Wen Shao,et al.  A data reduction method in formal fuzzy contexts , 2017, Int. J. Mach. Learn. Cybern..

[33]  Duo Pei,et al.  Attribute reduction in decision formal context based on homomorphism , 2011, Int. J. Mach. Learn. Cybern..

[34]  Samir Elloumi,et al.  Using minimal generators for composite isolated point extraction and conceptual binary relation coverage: Application for extracting relevant textual features , 2016, Inf. Sci..

[35]  Alain Gély Links between Modular Decomposition of Concept Lattice and Bimodular Decomposition of a Context , 2011, CLA.

[36]  Jozef Pócs,et al.  On concept reduction based on some graph properties , 2016, Knowl. Based Syst..

[37]  Jinhai Li,et al.  Knowledge reduction in decision formal contexts , 2011, Knowl. Based Syst..

[38]  Jinhai Li,et al.  An information fusion technology for triadic decision contexts , 2016, Int. J. Mach. Learn. Cybern..

[39]  Witold Pedrycz,et al.  A completeness analysis of frequent weighted concept lattices and their algebraic properties , 2012, Data Knowl. Eng..

[40]  Brian A. Davey,et al.  An Introduction to Lattices and Order , 1989 .

[41]  David Forge,et al.  Incremental Construction of Alpha Lattices and Association Rules , 2010, KES.

[42]  Sérgio M. Dias,et al.  Reducing the Size of Concept Lattices: The JBOS Approach , 2010, CLA.

[43]  Gerd Stumme,et al.  Computing iceberg concept lattices with T , 2002, Data Knowl. Eng..

[44]  Sadok Ben Yahia,et al.  QualityCover: Efficient binary relation coverage guided by induced knowledge quality , 2016, Inf. Sci..

[45]  Jonas Poelmans,et al.  Knowledge representation and processing with formal concept analysis , 2013, WIREs Data Mining Knowl. Discov..

[46]  Radim Belohlávek,et al.  Impact of Boolean factorization as preprocessing methods for classification of Boolean data , 2014, Annals of Mathematics and Artificial Intelligence.

[47]  Uwe Ryssel,et al.  Fast algorithms for implication bases and attribute exploration using proper premises , 2014, Annals of Mathematics and Artificial Intelligence.

[48]  V. Snasel,et al.  Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions , 2007, 2007 Innovations in Information Technologies (IIT).

[49]  Michal Krupka,et al.  Subset-Generated Complete Sublattices as Concept Lattices , 2015, CLA.

[50]  Douglas R. Vogel,et al.  Complexity Reduction in Lattice-Based Information Retrieval , 2005, Information Retrieval.

[51]  Nathalie Pernelle,et al.  ZooM: a nested Galois lattices-based system for conceptual clustering , 2002, J. Exp. Theor. Artif. Intell..

[52]  Camille Roth,et al.  Approaches to the Selection of Relevant Concepts in the Case of Noisy Data , 2010, ICFCA.

[53]  Luis E. Zárate,et al.  FCANN: A new approach for extraction and representation of knowledge from ANN trained via Formal Concept Analysis , 2008, Neurocomputing.

[54]  Ivo Düntsch,et al.  Simplifying Contextual Structures , 2015, PReMI.

[55]  Luis E. Zárate,et al.  Canonical Computational Models Based on Formal Concept Analysis for Social Network Analysis and Representation , 2015, 2015 IEEE International Conference on Web Services.

[56]  Jesús Medina,et al.  Relating attribute reduction in formal, object-oriented and property-oriented concept lattices , 2012, Comput. Math. Appl..

[57]  Michael D. Rice,et al.  Clusters, Concepts, and Pseudometrics , 2001, MFCSIT.

[58]  Vilém Vychodil,et al.  Formal Concept Analysis With Background Knowledge: Attribute Priorities , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[60]  Sérgio M. Dias,et al.  Concept lattices reduction: Definition, analysis and classification , 2015, Expert Syst. Appl..

[61]  Yves Bastide,et al.  Computing Proper Implications , 2001 .

[62]  Radim Belohlávek,et al.  Selecting Important Concepts Using Weights , 2011, ICFCA.