Knowledge discovery in data using formal concept analysis and random projections

Knowledge discovery in data using formal concept analysis and random projections In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

[1]  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).

[2]  C. Kumar,et al.  Latent Semantic Indexing using eigenvalue analysis for efficient information retrieval , 2006 .

[3]  Przemyslaw Kazienko,et al.  Mining Indirect Association Rules for Web Recommendation , 2009, Int. J. Appl. Math. Comput. Sci..

[4]  Cherukuri Aswani Kumar,et al.  Reducing data dimensionality using random projections and fuzzy k-means clustering , 2011, Int. J. Intell. Comput. Cybern..

[5]  J. Deogun,et al.  Concept approximations based on rough sets and similarity measures , 2001 .

[6]  Rokia Missaoui,et al.  Formal Concept Analysis for Knowledge Discovery and Data Mining: The New Challenges , 2004, ICFCA.

[7]  P. Filzmoser,et al.  Random projection experiments with chemometric data , 2010 .

[8]  Samir Elloumi,et al.  A multi-level conceptual data reduction approach based on the Lukasiewicz implication , 2004, Inf. Sci..

[9]  V. Horner.  Developing a consumer health informatics decision support system using formal concept analysis , 2008 .

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

[11]  Cherukuri Aswani Kumar,et al.  Analysis of unsupervised dimensionality reduction techniques , 2009, Comput. Sci. Inf. Syst..

[12]  Rudolf Wille,et al.  Why can concept lattices support knowledge discovery in databases? , 2002, J. Exp. Theor. Artif. Intell..

[13]  S. Srinivas,et al.  A NOTE ON WEIGHTED FUZZY K-MEANS CLUSTERING FOR CONCEPT DECOMPOSITION , 2010, Cybern. Syst..

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

[15]  Bernhard Ganter,et al.  Formal Concept Analysis , 2013 .

[16]  Ch. Aswani Kumar.  Deceiving Communication Links on an Organization Email Corpus , 2011 .

[17]  Dimitris Achlioptas,et al.  Database-friendly random projections: Johnson-Lindenstrauss with binary coins , 2003, J. Comput. Syst. Sci..

[18]  Rudolf Wille,et al.  Formal Concept Analysis as Applied Lattice Theory , 2006, CLA.

[19]  G. D. Oosthuizen,et al.  Knowledge discovery in databases using lattices , 1997 .

[20]  Claudio Carpineto,et al.  Concept data analysis - theory and applications , 2004 .

[21]  Ronald L. Breiger,et al.  Lattices and dimensional representations: matrix decompositions and ordering structures , 2002, Soc. Networks.

[22]  Gerd Stumme,et al.  Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods , 1998, PKDD.

[23]  Heikki Mannila,et al.  Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.

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

[25]  Uta Priss.  Formal concept analysis in information science , 2006 .

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

[27]  Jonas Poelmans,et al.  Formal Concept Analysis in Knowledge Discovery: A Survey , 2010, ICCS.

[28]  Partha Ghosh,et al.  Fuzzy graph representation of a fuzzy concept lattice , 2010, Fuzzy Sets Syst..

[29]  Gerd Stumme,et al.  Formal Concept Analysis , 2009, Handbook on Ontologies.

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

[31]  Ch. Aswani Kumar,et al.  MINING ASSOCIATIONS IN HEALTH CARE DATA USING FORMAL CONCEPT ANALYSIS AND SINGULAR VALUE DECOMPOSITION , 2010 .

[32]  Ch. Aswanikumar,et al.  Concept lattice reduction using fuzzy K-Means clustering , 2010, Expert Syst. Appl..

[33]  Ming-Wen Shao,et al.  Reduction method for concept lattices based on rough set theory and its application , 2007, Comput. Math. Appl..